Machine Learning (Theory)

9/29/2012

Vowpal Wabbit, version 7.0

A new version of VW is out. The primary changes are:

  1. Learning Reductions: I’ve wanted to get learning reductions working and we’ve finally done it. Not everything is implemented yet, but VW now supports direct:
    1. Multiclass Classification –oaa or –ect.
    2. Cost Sensitive Multiclass Classification –csoaa or –wap.
    3. Contextual Bandit Classification –cb.
    4. Sequential Structured Prediction –searn or –dagger

    In addition, it is now easy to build your own custom learning reductions for various plausible uses: feature diddling, custom structured prediction problems, or alternate learning reductions. This effort is far from done, but it is now in a generally useful state. Note that all learning reductions inherit the ability to do cluster parallel learning.

  2. Library interface: VW now has a basic library interface. The library provides most of the functionality of VW, with the limitation that it is monolithic and nonreentrant. These will be improved over time.
  3. Windows port: The priority of a windows port jumped way up once we moved to Microsoft. The only feature which we know doesn’t work at present is automatic backgrounding when in daemon mode.
  4. New update rule: Stephane visited us this summer, and we fixed the default online update rule so that it is unit invariant.

There are also many other small updates including some contributed utilities that aid the process of applying and using VW.

Plans for the near future involve improving the quality of various items above, and of course better documentation: several of the reductions are not yet well documented.

3/15/2010

The Efficient Robust Conditional Probability Estimation Problem

I’m offering a reward of $1000 for a solution to this problem. This joins the cross validation problem which I’m offering a $500 reward for. I believe both of these problems are hard but plausibly solvable, and plausibly with a solution of substantial practical value. While it’s unlikely these rewards are worth your time on an hourly wage basis, the recognition for solving them definitely should be :-)

The Problem

The problem is finding a general, robust, and efficient mechanism for estimating a conditional probability P(y|x) where robustness and efficiency are measured using techniques from learning reductions.

In particular, suppose we have access to a binary regression oracle B which has two interfaces—one for specifying training information and one for testing. Training information is specified as B(x’,y’) where x’ is a feature vector and y’ is a scalar in [0,1] with no value returned. Testing is done according to B(x’) with a value in [0,1] returned.

A learning reduction consists of two algorithms R and R-1 which transform examples from the original input problem into examples for the oracle and then transform the oracle’s predictions into a prediction for the original problem.

The algorithm R takes as input a single example (x,y) where x is an feature vector and y is a discrete variable taking values in {1,…,k}. R then specifies a training example (x’,y’) for the oracle B. R can then create another training example for B based on all available information. This process repeats some finite number of times before halting without returning information.

A basic observation is that for any oracle algorithm, a distribution D(x,y) over multiclass examples and a reduction R induces a distribution over a sequence (x’,y’)* of oracle examples. We collapse this into a distribution D’(x’,y’) over oracle examples by drawing uniformly from the sequence.

The algorithm R-1 takes as input a single example (x,y) and returns a value in [0,1] after using (only) the testing interface of B zero or more times.

We measure the power of an oracle and a reduction according to squared-loss regret. In particular we have:


reg(D,R-1)=E(x,y)~ D[(R-1(x,y)-D(y|x))2]

and similarly letting mx’=E(x’,y’)~ D’[y'].

reg(D’,B)=E(x’,y’)~ D’(B(x’) – mx’)2

The open problem is to specify R and R-1 satisfying the following theorem:

For all multiclass distributions D(x,y), for all binary oracles B: The computational complexity of R and R-1 are O(log k)
and


reg(D,R-1) < = C reg(D’,B)

where C is a universal constant.

Alternatively, this open problem is satisfied by proving there exists no deterministic algorithms R,R-1 satisfying the above theorem statement.

Motivation

The problem of conditional probability estimation is endemic to machine learning applications. In fact, in some branches of machine learning, this is simply considered “the problem”. Typically conditional probability estimation is done in situations where the conditional probability of only one bit is required, however there are a growing number of applications where a well-estimated conditional probability over a more complex object is required. For example, all known methods for solving general contextual bandit problems require knowledge of or good estimation of P(a | x) where a is an action.

There is a second intrinsic motivation which is matching the lower bound. No method faster than O(log k) can be imagined because the label y requires log2 k bits to specify and hence read. Similarly it’s easy to prove no learning reduction can provide a regret ratio with C<1.

The motivation for using the learning reduction framework to specify this problem is a combination of generality and the empirical effectiveness in application of learning reductions. Any solution to this will be general because any oracle B can be plugged in, even ones which use many strange kinds of prior information, features, and active multitask hierachical (insert your favorite adjective here) structure.

Related Results

The state of the art is summarized here which shows it’s possible to have a learning reduction satisfying the above theorem with either:

  1. C replaced by (log2 k)2 (using a binary tree structure)
  2. or the computational time increased to O(k) (using an error correcting code structure).

Hence, answering this open problem in the negative shows that there is an inherent computation vs. robustness tradeoff.

There are two other closely related problems, where similar analysis can be done.

  1. For multiclass classification, where the goal is predicting the most likely class, a result analogous to the open problem is provable using error correcting tournaments.
  2. For multiclass classification in a partial label setting, no learning reduction can provide a constant regret guarantee.

Silly tricks that don’t work

Because Learning reductions are not familiar to everyone, It’s helpful to note certain tricks which do not work here to prevent false leads and provide some intuition.

Ignore B‘s predictions and use your favorite learning algorithm instead.

This doesn’t work, because the quantification is for all D. Any specified learning algorithm will have some D on which it has nonzero regret. On the other hand, because R calls the oracle at least once, there is a defined induced distribution D’. Since the theorem must hold for all D and B, it must hold for a D your specified learning algorithm fails on and for a B for which reg(D’,B)=0 implying the theorem is not satisfied.

Feed random examples into B and vacuously satisfy the theorem by making sure that the right hand side is larger than a constant.

This doesn’t work because the theorem is stated in terms of squared loss regret rather than squared loss. In particular, if the oracle is given examples of the form (x’,y’) where y’ is uniformly at random either 0 or 1, any oracle specifying B(x’)=0.5 has zero regret.

Feed pseudorandom examples into B and vacuously satisfy the theorem by making sure that the right hand side is larger than a constant.

This doesn’t work, because the quantification is “for all binary oracles B”, and there exists one which, knowing the pseudorandom seed, can achieve zero loss (and hence zero regret).

Just use Boosting to drive the LHS to zero.

Boosting theorems require a stronger oracle—one which provides an edge over some constant baseline for each invocation. The oracle here is not limited in this fashion since it could completely err for a small fraction of invocations.

Take an existing structure, parameterize it, randomize over the parameterization, and then average over the random elements.

Employing this approach is not straightforward, because the average in D’ is over an increased number of oracle examples. Hence, at a fixed expected (over oracle examples) regret, the number of examples allowed to have a large regret is increased.

1/24/2010

Specializations of the Master Problem

One thing which is clear on a little reflection is that there exists a single master learning problem capable of encoding essentially all learning problems. This problem is of course a very general sort of reinforcement learning where the world interacts with an agent as:

  1. The world announces an observation x.
  2. The agent makes a choice a.
  3. The world announces a reward r.

The goal here is to maximize the sum of the rewards over the time of the agent. No particular structure relating x to a or a to r is implied by this setting so we do not know effective general algorithms for the agent. It’s very easy to prove lower bounds showing that an agent cannot hope to succeed here—just consider the case where actions are unrelated to rewards. Nevertheless, there is a real sense in which essentially all forms of life are agents operating in this setting, somehow succeeding. The gap between these observations drives research—How can we find tractable specializations of the master problem general enough to provide an effective solution in real problems?

The process of specializing is a tricky business, as you want to simultaneously achieve tractable analysis, sufficient generality to be useful, and yet capture a new aspect of the master problem not otherwise addressed. Consider: How is it even possible to choose a setting where analysis is tractable before you even try to analyze it? What follows is my mental map of different specializations.

Online Learning

The online learning setting is perhaps the most satisfying specialization more general than standard batch learning at present, because it turns out to additionally provide tractable algorithms for many batch learning settings.

Standard online learning models specialize in two ways: You assume that the choice of action in step 2 does not influence future observations and rewards, and you assume additional information is available in step 3, a retrospectively available reward for each action. The algorithm for an agent in this setting typically has a given name—gradient descent, weighted majority, Winnow, etc…

The general algorithm here is a more refined version of follow-the-leader than in batch learning, with online update rules. An awesome discovery about this setting is that it’s possible to compete with a set of predictors even when the world is totally adversarial, substantially strengthening our understanding of what learning is and where it might be useful. For this adversarial setting, the algorithm alters into a form of follow-the-perturbed leader, where the learning algorithm randomizes it’s action amongst the set of plausible alternatives in order to defeat an adversary.

The standard form of argument in this setting is a potential argument, where at each step you show that if the learning algorithm performs badly, there is some finite budget from which an adversary deducts it’s ability. The form of the final theorem is that you compete with the accumulated reward of a set any one-step policies h:X – > A, with a dependence log(#policies) or weaker in regret, a measure of failure to compete.

A good basic paper to read here is:
Nick Littlestone and Manfred Warmuth, The Weighted Majority Algorithm, which shows the basic information-theoretic claim clearly. Vovk‘s page on aggregating algorithms is also relevant, although somewhat harder to read.

Provably computationally tractable special cases all have linear structure, either on rewards or policies. Good results are often observed empirically by applying backpropagation for nonlinear architectures, with the danger of local minima understood.

Bandit Analysis

In the bandit setting, step 1 is omitted, and the difficulty of the problem is weakened by assuming that action in step (2) don’t alter future rewards. The goal is generally to compete with all constant arm strategies.

Analysis in this basic setting started very specialized with Gittin’s Indicies and gradually generalized over time to include IID and fully adversarial settings, with EXP3 a canonical algorithm. If there are k strategies available, the standard theorem states that you can compete with the set of all constant strategies up to regret k. The most impressive theoretical discovery in this setting is that the dependence on T, the number of timesteps, is not substantially worse than supervised learning despite the need to explore.

Given the dependence on k all of these algorithms are computationally tractable.

However, the setting is flawed, because the set of constant strategies is inevitably too weak in practice—it’s an example of optimal decision making given that you ignore almost all information. Adding back the observation in step 1 allows competing with a large set of policies, while the regret grows only as log(#policies) or weaker. Canonical algorithms here are EXP4 (computationally intractable, but information theoretically near-optimal), Epoch-Greedy (computationally tractable given an oracle optimizer), and the Offset Tree providing a reduction to supervised binary classification.

MDP analysis

A substantial fraction of reinforcement learning has specialized on the Markov Decision Process setting, where the observation x is a state s, which is a sufficient statistic for predicting all future observations. Compared to the previous settings, dealing with time dependence is explicitly required, but learning typically exists in only primitive forms.

The first work here was in the 1950′s where the actual MDP was assumed known and the problem was simply computing a good policy, typically via dynamic programming style solutions. More recently, principally in the 1990′s, the setting where the MDP was not assumed known was analyzed. A very substantial theoretical advancement was the E3 algorithm which requires only O(S2A) experience to learn a near-optimal policy where the world is an MDP with S state and A actions per state. A further improvement on this is Delayed Q-Learning, where only O(SA) experience is required. There are many variants on the model-based approach and not much for the model-free approach. Lihong Li‘s thesis probably has the best detailed discussion at present.

There are some unsatisfactory elements of the analysis here. First, I’ve suppressed the dependence on the definition of “approximate” and the typical time horizon, for which the dependence is often bad and the optimality is unclear. The second is the dependence on S, which is intuitively unremovable, with this observation formalized in the lower bound Sham and I worked on (section 8.6 of Sham’s thesis). Empirically, these and related algorithms are often finicky, because in practice the observation isn’t a sufficient statistic and the number of states isn’t small, so approximating things as such is often troublesome.

A very different variant of this setting is given by Control theory, which I know less about than I should. The canonical setting for control theory is with a known MDP having linear transition dynamics. More exciting are the system identification problems where the system must be first identified. I don’t know any good relatively assumption free results for this setting.

Oracle Advice Shortcuts

Techniques here specialize the setting to situations in which some form of oracle advice is available when a policy is being learned. A good example of this is an oracle which provides samples from the distribution of observations visited by a good policy. Using this oracle, conservative policy iteration is guaranteed to perform well, so long as a base learning algorithm can predict well. This algorithm was refined and improved a bit by PSDP, which works via dynamic programming, improving guarantees to work with regret rather than errors.

An alternative form of oracle is provide by access to a good policy at training time. In this setting, Searn has similar provable guarantees with a similar analysis.

The oracle based algorithms appear to work well anywhere these oracles are available.

Uncontrolled Delay

In the uncontrolled delay setting, step (2) is removed, and typically steps (1) and (3) are collapsed into one observation, where the goal becomes state tracking. Most of the algorithms for state tracking are heavily model dependent, implying good success within particular domains. Examples include Kalman filters, hidden markov models, and particle filters which typical operate according to an explicit probabilistic model of world dynamics.

Relatively little is known for a nonparametric version of this problem. One observation is that the process of predicting adjacent observations well forms states as a byproduct when the observations are sufficiently rich as detailed here.

A basic question is: What’s missing from the above? A good answer is worth a career.

5/2/2009

Wielding a New Abstraction

This post is partly meant as an advertisement for the reductions tutorial Alina, Bianca, and I are planning to do at ICML. Please come, if you are interested.

Many research programs can be thought of as finding and building new useful abstractions. The running example I’ll use is learning reductions where I have experience. The basic abstraction here is that we can build a learning algorithm capable of solving classification problems up to a small expected regret. This is used repeatedly to solve more complex problems.

In working on a new abstraction, I think you typically run into many substantial problems of understanding, which make publishing particularly difficult.

  1. It is difficult to seriously discuss the reason behind or mechanism for abstraction in a conference paper with small page limits. People rarely see such discussions and hence have little basis on which to think about new abstractions. Another difficulty is that when building an abstraction, you often don’t know the right way to state things.

    Here’s my current attempt: The process of abstraction for learning reductions can start with sample complexity bounds (or online learning against an adversary analysis). A very simple sample complexity bound is that for all sets of hypotheses H, for all distributions D on examples (x,y), and for all confidence parameters d

    Pr(x,y)m~Dm(for all h in H: |e(h,D)-e(h,(x,y)m)| < (ln( |H|/ d )/m)0.5 ) > 1 – d

    Here (x,y)m is a sequence of m IID samples, e(h,D) is the error rate of h on D and e(h,(x,y)m) is the empirical error rate of h on the set of IID samples.

    The previous bound is a very simple example, and yet remarkably complex both to state and to interpret—many people have been lost by the meaning of d. The impact of this complexity is that it is difficult to effectively use these bounds in practical learning algorithm design, particularly in solving more complex learning problems where much more than one bit of prediction is required. This was a central frustration that I ran into in my thesis work. Some progress has been made since then, but it is still quite difficult. The abstraction in the learning reduction setting is:

    1. You throw away d, because it only has a logarithmic dependence anyways.
    2. You eliminate H and m on the theory that intelligent choices for H and m are made in practice.
    3. You eliminate the IID assumption, because it is no longer needed to define things

    The statement then is

    e(A((x,y)m),D)-e(h*,D) < eps

    where A() is the hypothesis output by the learning algorithm, h* is the best possible predictor, and eps is used to parameterize the theorems. This abstraction is radical in some sense, but something radical was needed to yield tractable and useful analysis on the complex problems people need to solve in practice.

  2. A consequence of lack of familiarity, is that people often misread. In reading a paper, there is a temptation to not read carefully and fill in your understanding of things. Most of the time this works out well, but not here. For example, we saw many instances where people inserted IID sample assumptions or other things that simply weren’t there.
  3. Once you get past the lack of familiarity and misunderstandings, there is a feeling that the new abstraction is cheating. To some extent I understand, as I remember learning about abstractions in class, and I remember feeling that they were in some real sense cheating by dropping important details. For example:
    1. Big-O notation provides an upper bound specified up to constants. For example O(log n) computational complexity means there exists a constant c such that the number of operations requires is less than c log n. Big-O can be abused by hiding “constants” larger than the plausible values for the parameters. In machine learning, a particularly egregious case occurs in Bandit analysis where the punchline of some papers is “logarithmic regret”, hiding an arbitrarily large problem dependent constant.
    2. TCP provides a mechanism for reliable transport over an unreliable network. It is a very commonly used mechanism for sending information over the internet—you used TCP in reading this. TCP is both a programming construct and a mechanism for abstracting communicating over a network. The TCP abstraction is broken when the network is too unreliable for it to recover, such as on sketchy wireless networks where the programmer built for the TCP abstraction which wasn’t delivered.
    3. Dimensional analysis is a technique for quick analysis in physics. The basic idea is to just look at the units when estimating some quantity and combine them to get the right unit answer. For example, to compute the distance d traveled after time t with acceleration a, you simply use at2, since that formula is the only way to combine a with units of distance/time2 and t with units of time to get units of distance. This answer is off by a factor of 2 from what a more detailed analysis using integration yields, which is typical. Dimensional analysis can be misleading when the constants are very large. One example is in Gravitation where there is a table with time and distance equated since they are related by a constant—the speed of light 3*108 m/s. For example, E=mc2 becomes E=m.

    Although the above breakages are real, the usefulness of these abstractions, in terms of allowing us to quickly think about and make decisions more than offsets the drawbacks. Indeed, even the breakages stated above are thought provoking or useful enough that I can’t even say it is wrong to consider them. This property that abstractions can be abused is generically essential to the process of abstraction itself. Abstraction is about neglecting details, and when these details are not neglectable, the abstraction is abused or ineffective. Because of this, any abstraction is insufficient for analyzing and solving real problems where the neglected details matter.

    Just as for these abstractions, the learning reduction abstraction can be abused—the chosen learning algorithm can be pathetic yielding vacuous bounds, or the reduction can scramble the feature information with an encryption algorithm making it so no reasonable learning algorithm could yield other than pathetic performance. Similarly, there are situations in which I don’t know how to effectively use a learning reduction to build a learning algorithm, and it seems implausible that observation changes as more is learned in the future.

For a good abstraction, the drawbacks are matched by the advantages. The principle advantage is that there is a new way to examine and solve problems. This has several interesting effects.

  1. A good abstraction can capture a more complete specification of the problem. As an example, the sample complexity view of learning is broken in practice, because when insufficient performance is achieved people choose a different set of hypotheses H by throwing in additional features or choosing a different learning algorithm. Capturing this process in the sample complexity view requires an additional level of complexity. In the reduction view, this is entirely natural, because any means for achieving a better generalization—more/better features, a better learning algorithm, a better prior, sticking a human in the learning process, etc… are legitimate. This is particularly powerful when architecting solutions, providing a partial answer to the “What?” question Yehuda pointed out.
  2. A higher level abstraction can let you accidentally solve problems in other areas as well. A good example of this is error correcting tournaments which are useful for tournament design to select the best player/team/paper in real tournaments. Recently, I was amused to learn that a standard betting procedure for basketball tournaments exactly mirrors the importance weights suggested for the final elimination of ECTs. The first phase of ECTs provides a sound and practical method to seed a final elimination tournament, eliminating the need for (and biases of) a committee.
  3. Perhaps the most interesting effect is that the new abstraction can aid you in finding effective solutions to new problems. For learning reductions, there are about 3 compelling instances I’ve seen so far.
    1. Given training-time access to a good policy oracle, Searn provides a method for decomposing any complex prediction problem into simple problems, such that low regret solutions to the simple problems imply a low regret solution to the original problem. While Searn competes well (computationally and prediction-wise) with existing methods for linear chain style structured prediction, it really shines on more complex problems. Hal used Searn for automatic document summarization (see section 6.2) which previously wasn’t really solved via ML. More generally, when I learn about the details of other complex prediction systems for machine translation or vision, the base algorithms are tweaked, typically in ways that Searn would suggest. This suggests that Searn formalizes and automates the intuitions of practical people.
    2. The “one step RL” reduction in Bianca‘s thesis (page 119) provided tractable and effective approaches to learning in partial feedback problems where only the loss of a chosen label is learned. An even simpler reduction exists as a matter of folklore—estimate the the value of each label and then take an argmax. However, we have found classification approaches generally work better, where applicable, and as the theory suggests.
    3. Many commonly used algorithms for prediction have a running time linear (or worse) in the number of labels with decision trees a good exception. While simply predicting faster isn’t normally solving a “new problem”, an exponential improvement in computational time seems to merit this description because it allows entirely new kinds of applications. It turns out that it is both very easy to do logarithmic time prediction wrong, and that this problem is often fixable. Furthermore, it appears logarthmic time prediction can really work in practice over very many labels.

When we started working on learning reductions, I had no idea what either the difficulties or rewards were going to be—it simply seemed like a natural and compelling direction of investigation. Given the substantial difficulties encountered, it’s not at all clear that this pursuit was personally worthwhile. It has cost much time which could have been put to good use in other ways.

On the other hand, the advantages are also substantial. I’ve learned something about architecting solutions to problems, both expanding the domain of application for the field and providing a personal edge that I can bring to many conversations about ML. It’s also progress towards the AI goal, which interests me. When I think of what I could have worked on instead to achieve these goals, I don’t have any more compelling answer yet. Learning reductions seem to have accomplished more per unit thought than any other theoretical approach I can identify over the last 5 or 6 years. Furthermore, they are composable by design, so they should stay relevant (and perhaps even become more so), when people use an online active deep semisupervised probabilistic convolutional algorithm to solve a problem, particularly for complex problems.

As I said at the beginning, please join us for the tutorial, if you are interested.

3/8/2009

Prediction Science

One view of machine learning is that it’s about how to program computers to predict well. This suggests a broader research program centered around the more pervasive goal of simply predicting well.
There are many distinct strands of this broader research program which are only partially unified. Here are the ones that I know of:

  1. Learning Theory. Learning theory focuses on several topics related to the dynamics and process of prediction. Convergence bounds like the VC bound give an intellectual foundation to many learning algorithms. Online learning algorithms like Weighted Majority provide an alternate purely game theoretic foundation for learning. Boosting algorithms yield algorithms for purifying prediction abiliity. Reduction algorithms provide means for changing esoteric problems into well known ones.
  2. Machine Learning. A great deal of experience has accumulated in practical algorithm design from a mixture of paradigms, including bayesian, biological, optimization, and theoretical.
  3. Mechanism Design. The core focus in game theory is on equilibria, mostly typically Nash equilibria, but also many other kinds of equilibria. The point of equilibria, to a large extent, is predicting how agents will behave. When this is employed well, principally in mechanism design for auctions, it can be a very powerful concept.
  4. Prediction Markets. The basic idea in a prediction market is that commodities can be designed so that their buy/sell price reflects a form of wealth-weighted consensus estimate of the probability of some event. This is not simply mechanism design, because (a) the thin market problem must be dealt with and (b) the structure of plausible guarantees is limited.
  5. Predictive Statistics. Part of statistics focuses on prediction, essentially becoming indistinguishable from machine learning. The canonical example of this is tree building algorithms such as CART, random forests, and some varieties of boosting. Similarly the notion of probability, counting, and estimation are all handy.
  6. Robust Search. I have yet to find an example of robust search which isn’t useful—and there are several varieties. This includes active learning, robust min finding, and (more generally) compressed sensing and error correcting codes.

The lack of unification is fertile territory for new research, so perhaps it’s worthwhile to think about how these different research programs might benefit from each other.

  1. Learning Theory. The concept of mechanism design is mostly missing from learning theory, but it is sure to be essential when interactive agents are learning. We’ve found several applications for robust search as well as new settings for robust search such as active learning, and error correcting tournaments, but there are surely others.
  2. Machine Learning and Predictive Statistics. Machine learning has been applied to auction design. There is a strong relationship between incentive compatibility and choice of loss functions, both for choosing proxy losses and approximating the real loss function imposed by the world. It’s easy to imagine designer loss functions from the study of incentive compatibility mechanisms giving learning algorithm an edge. I found this paper thought provoking that way. Since machine learning and information markets share a design goal, are there hybrid approaches which can outperform either?
  3. Mechanism Design. There are some notable similarities between papers in ML and mechanism design. For example there are papers about learning on permutations and pricing in combinatorial markets. I haven’t yet taken the time to study these carefully, but I could imagine that one suggests advances for the other, and perhaps vice versa. In general, the idea of using mechanism design with context information (as is done in machine learning), could also be extremely powerful.
  4. Prediction Markets. Prediction markets are partly an empirical field and partly a mechanism design field. There seems to be relatively little understanding about how well and how exactly information from multiple agents is supposed to interact to derive a good probability estimate. For example, the current global recession reminds us that excess leverage is a very bad idea. The same problem comes up in machine learning and is solved by the weighted majority algorithm (and even more thoroughly by the hedge algorithm). Can an information market be designed with the guarantee that an imperfect but best player decides the vote after not-too-many rounds? How would this scale as a function of the ratio of a participants initial wealth to the total wealth?
  5. Robust Search. Investigations into robust search are extremely diverse, essentially only unified in a mathematically based analysis. For people interested in robust search, machine learning and information markets provide a fertile ground for empirical application and new settings. Can all mechanisms for robust search be done with context information, as is common in learning? Do these approaches work empirically in machine learning or information markets?

There are almost surely many other interesting research topics and borrowable techniques here, and probably even other communities oriented around prediction. While the synthesis of these fields is almost sure to eventually happen, I’d like to encourage it sooner rather than later. For someone working on one of these branches, attending a conference on one of the other branches might be a good start. At a lesser time investment, Oddhead is a good start.

2/18/2009

Decision by Vetocracy

Few would mistake the process of academic paper review for a fair process, but sometimes the unfairness seems particularly striking. This is most easily seen by comparison:

Paper Banditron Offset Tree Notes
Problem Scope Multiclass problems where only the loss of one choice can be probed. Strictly greater: Cost sensitive multiclass problems where only the loss of one choice can be probed. Often generalizations don’t matter. That’s not the case here, since every plausible application I’ve thought of involves loss functions substantially different from 0/1.
What’s new Analysis and Experiments Algorithm, Analysis, and Experiments As far as I know, the essence of the more general problem was first stated and analyzed with the EXP4 algorithm (page 16) (1998). It’s also the time horizon 1 simplification of the Reinforcement Learning setting for the random trajectory method (page 15) (2002). The Banditron algorithm itself is functionally identical to One-Step RL with Traces (page 122) (2003) in Bianca‘s thesis with the epsilon greedy strategy and a multiclass perceptron with update scaled by the importance weight.
Computational Time O(k) per example where k is the number of choices O(log k) per example Lower bounds on the sample complexity of learning in this setting are a factor of k worse than for supervised learning, implying that many more examples may be needed in practice. Consequently, learning algorithm speed is more important than in standard supervised learning.
Analysis Incomparable. An online regret analysis showing that if a small hinge loss predictor exists, a bounded number of mistakes occur. Also, an algorithm independent analysis of the fully realizable case. Incomparable. A learning reduction analysis showing how the regret of any base classifier bounds policy regret. Also contains a lower bound and comparable analysis of all plausible alternative reductions.
Experiments 1 dataset, comparing with no other approaches to solving the problem. 13 datasets, comparing with 2 other approaches to solve the problem.
Outcome Accepted at ICML Rejected at ICML, NIPS, UAI, and NIPS.

The reviewers of the Banditron paper made the right call. The subject is interesting, and analysis of a new learning domain is of substantial interest. Real advances in machine learning often come as new domains of application. The talk was well attended and generated substantial interest. It’s also important to remember the reviewers of the two papers probably did not overlap, so there was no explicit preference for A over B.

Why was the Offset Tree rejected? One of these rejections is easily explained as a fluke—we ran into a reviewer at UAI who believes that learning by memorization is the way to go. I, and virtually all machine learning people, disagree but some reviewers at UAI aren’t interested or expert in machine learning.

The striking thing about the other 3 rejects is that they all contain a reviewer who doesn’t read the paper. Instead, the reviewer asserts that learning reductions are bogus because for an alternative notion of learning reduction, made up by the reviewer, an obviously useless approach yields a factor of 2 regret bound. I believe this is the same reviewer each time, because the alternative theorem statement drifted over the reviews fixing bugs we pointed out in the author response.

The first time we encountered this review, we assumed the reviewer was just cranky that day—maybe we weren’t quite clear enough in explaining everything as it’s always difficult to get every detail clear in new subject matter. I have sometimes had a very strong negative impression of a paper which later turned out to be unjustified upon further consideration. Sometimes when a reviewer is cranky, they change their mind after the authors respond, or perhaps later, or perhaps never but you get a new set of reviewers the next time.

The second time the review came up, we knew there was a problem. If we are generous to the reviewer, and taking into account the fact that learning reduction analysis is a relatively new form of analysis, the fear that because an alternative notion of reduction is vacuous our notion of reduction might also be vacuous isn’t too outlandish. Fortunately, there is a way to completely address that—we added an algorithm independent lower bound to the draft (which was the only significant change in content over the submissions). This lower bound conclusively proves that our notion of learning reduction is not vacuous as is the reviewer’s notion of learning reduction.

The review came up a third time. Despite pointing out the lower bound quite explicitly, the reviewer simply ignored it. This more-or-less confirms our worst fears. Some reviewer is bidding for the paper with the intent to torpedo review it. They are uninterested in and unwiling to read the content itself.

Shouldn’t author feedback address this? Not if the reviewer ignores it.

Shouldn’t Double Blind reviewing help? Not if the paper only has one plausible source. The general problem area and method of analysis were freely discussed on hunch.net. We withheld public discussion of the algorithm itself for much of the time (except for a talk at CMU) out of respect for the review process.

Why doesn’t the area chair/program chair catch it? It took us 3 interactions to get it, so it seems unrealistic to expect someone else to get it in one interaction. In general, these people are strongly overloaded and the reviewer wasn’t kind enough to boil down the essence of the stated objection as I’ve done above. Instead, they phrase it as an example and do not clearly state the theorem they have in mind or distinguish the fact that the quantification of that theorem differs from the quantification of our theorems. More generally, my observation is that area chairs rarely override negative reviews because:

  1. It risks their reputation since defending a criticized work requires the kind of confidence that can only be inspired by a thorough personal review they don’t have time for.
  2. They may offend the reviewer they invited to review and personally know.
  3. They figure that the average review is similar to the average perception/popularity by the community anyways.
  4. Even if they don’t agree with the reviewer, it’s hard to fully discount the review in their consideration.

I’ve seen these effects create substantial mental gymnastics elsewhere.

Maybe you just ran into a cranky reviewer 3 times randomly Maybe so. However, the odds seem low enough and the 1/2 year cost of getting another sample high enough, that going with the working hypothesis seems indicated.

Maybe the writing needs improving. Often that’s a reasonable answer for a rejection, but in this case I believe not. We’ve run the paper by several people, who did not have substantial difficulties understanding it. They even understand the draft well enough to make a suggestion or two. More generally, no paper is harder to read than the one you picked because you want to reject it.

What happens next? With respect to the Offset Tree, I’m hopeful that we eventually find reviewers who appreciate an exponentially faster algorithm, good empirical results, or the very tight and elegant analysis, or even all three. For the record, I consider the Offset Tree a great paper. It remains a substantial advance on the state of the art, even 2 years later, and as far as I know the Offset Tree (or the Realizable Offset Tree) consistently beat all reasonable contenders both in prediction and computational performance. This is rare and precious, as many papers tradeoff one for the other. It yields a practical algorithm applicable to real problems. It substantially addresses the RL to classification reduction problem. It also has the first nonconstant algorithm independent lower bound for learning reductions.

With respect to the reviewer, I expect remarkably little. The system is designed to protect reviewers, so they have virtually no responsibility for their decisions. This reviewer has a demonstrated capability to sabotage the review process at ICML and NIPS and a demonstrated willingness to continue doing so indefinitely. The process of bidding for papers and making up reasons to reject them seems tedious, but there is no fundamental reason why they can’t continue doing so for several decades if they remain active in academia.

This experience has substantially altered my understanding and appreciation of the review process at conferences. The bidding mechanism commonly used, coupled with responsibility-free reviewing is an invitation to abuse. A clever abusive reviewer can sabotage perhaps 5 papers per conference (out of 8 reviewed), while maintaining a typical average score. While I don’t believe most people choose papers with intent to sabotage, the capability is there and used by at least one person and possibly others. If, for example, 5% of reviewers are willing to abuse the process this way and there are 100 reviewers, every paper must survive 5 vetoes. If there are 200 reviewers, every paper must survive 10 vetoes. And if there are 400 reviewers, every paper must survive 20 vetoes. This makes publishing any paper that offends someone difficult. The surviving papers are typically inoffensive or part of a fad strong enough that vetoes are held back. Neither category is representative of high quality decision making. These observations suggest that the conference with the most reviewers tend strongly toward faddy and inoffensive papers, both of which often lack impact in the long term. Perhaps this partly explains why NIPS is so weak when people start citation counting. Conversely, this would suggest that smaller conferences and workshops have a natural advantage. Similarly, the reviewing style in theory conferences seems better—the set of bidders for any paper is substantially smaller, implying papers must survive fewer vetos.

This decision making process can be modeled as a group of n decision makers, each of which has the opportunity to veto any action. When n is relatively small, this decision making process might work ok, depending on the decision makers, but as n grows larger, it’s difficult to imagine a worse decision making process. The closest representatives outside of academia I know are deeply bureacratic governments and other large organizations where many people must sign off on something before it takes place. These vetocracies are universally frustrating to interact with. A reasonable conjecture is that any decision making process with a large veto number has poor characteristics.

A basic question is: Is a vetocracy inevitable for large organizations? I believe the answer is no. The basic observation is that the value of n can be logarithmic in the number of participants in an organization rather than linear, as per reviewing under a bidding process. An essential force driving vetocracy creation is a desire to offload responsibility for decisions, so there is no clear decision maker. A large organization not deciding by vetocracy must have a very different structure, with clearly dilineated responsibility.

NIPS provides an almost perfect natural experiment in it’s workshop organization, which involves the very same community of people and subject matter, yet works in a very different manner. There are one or two workshop chairs who are responsible for selecting amongst workshop proposals, after which the content of the workshop is entirely up to the workshop organizers. If a workshop is rejected, it’s clear who is at fault, and if a workshop presentation is rejected, it is often clear by who. Some workshop chairs use a small set of reviewers, but even then the effective veto number remains small. Similarly, if a workshop ends up a flop, it’s relatively easy to see who to blame—either the workshop chair for not predicting it, or the organizers for failing to organize. I can’t think of a single time when I attended both the workshops and the conference that the workshops were less interesting than the conference. My understanding is that this observation is common. Given this discussion, it will be particularly interesting to see how the review process Michael and Leon setup for ICML this year pans out, as it is a system with notably more responsibility assignment than in previous years.

Journals end up looking relatively good with respect to vetocracy avoidance. The ones I’m familiar with have a chief editor who bears responsibility for routing papers to an action editor, who bears responsibility for choosing good reviewers. Every agent except the reviewers is often known by the authors, and the reviewers don’t act as additional vetoers in nearly as strong a manner as reviewers with the opportunity to bid.

This experience has also altered my view of blogging and research. On one hand, I’m very enthusiastic about research in general, and my research in particular, where we are regularly cracking conventionally impossible problems. On the other hand, it seems that some small number of people viewing a discussion silently decide they don’t like it, and veto it given the opportunity. It only takes one to turn strong paper into a years-long odyssey, so public discussion of research directions and topics in a vetocracy is akin to voluntarily wearing a “kick me” sign. While this a problem for me, I expect it to be even worse for the members of a vetocracy in the long term.

It’s hard to imagine any research community surviving without a serious online presence. When a prospective new researcher looks around at existing research, if they don’t find serious online discussion, they’ll assume it doesn’t exist under the “not on the internet so it doesn’t exist” principle. This will starve a field of new people. More generally, there is an opportunity to get feedback about research directions and problems much more rapidly than is otherwise possible, allowing us to avoid research on dead end topics which are pervasive. At some point, it may even seem that people not willing to discuss their research simply avoid doing so because it is critically lacking in one way or another. Since a vetocracy creates a substantial disincentive to discuss research directions online, we can expect that communities sticking with decision by vetocracy to be at a substantial disadvantage.

2/4/2009

Optimal Proxy Loss for Classification

Many people in machine learning take advantage of the notion of a proxy loss: A loss function which is much easier to optimize computationally than the loss function imposed by the world. A canonical example is when we want to learn a weight vector w and predict according to a dot product fw(x)= sumi wixi
where optimizing squared loss (y-fw(x))2 over many samples is much more tractable than optimizing 0-1 loss I(y = Threshold(fw(x) – 0.5)).

While the computational advantages of optimizing a proxy loss are substantial, we are curious: which proxy loss is best? The answer of course depends on what the real loss imposed by the world is. For 0-1 loss classification, there are adherents to many choices:

  1. Log loss. If we confine the prediction to [0,1], we can treat it as a predicted probability that the label is 1, and measure loss according to log 1/p’(y|x) where p’(y|x) is the predicted probability of the observed label. A standard method for confining the prediction to [0,1] is logistic regression which exponentiates the dot product and normalizes.
  2. Squared loss. The squared loss approach (discussed above) is also quite common. It shares the same “proper scoring rule” semantics as log loss: the optimal representation-independent predictor is the conditional probability of the label y given the features x.
  3. Hinge loss. For hinge loss, you optimize max(0, 1- 4 (y – 0.5) (fw(x) – 0.5) ). The form of hinge loss is slightly unfamiliar, because the label is {0,1} rather than {-1,1}. The optimal prediction for hinge loss is not the probability of y given x but rather some value which is at least 1 if the most likely label is 1 and 0 or smaller if the most likely label is 0. Hinge loss was popularized with support vector machines. Hinge loss is not a proper scoring rule for mean, but since it does get the sign right, using it for classification is reasonable.

Many people have made qualitative arguments about why one loss is better than another. For example see Yaroslav’s old post for an argument about the comparison of log loss and hinge loss and why hinge loss might be better. In the following, I make an elementary quantitative argument.

Log loss is qualitatively dissimilar from the other two, because it is unbounded on the range of interest. Restated, there is no reason other than representational convenience that fw(x) needs to take a value outside of the interval [0,1] for squared loss or hinge loss. In fact, we can freely reduce these losses by considering instead the function fw‘(x) = max(0,min(1,fw(x))). The implication is that optimization of log loss can be unstable in ways that optimization of these other losses is not. This can be stated precisely by noting that sample complexity bounds (simple ones here) for 0-1 loss hold for fw‘(x) under squared or hinge loss, but the same theorem statement does not hold for log loss without additional assumptions. Since stability and convergence are of substantial interest in machine learning, this suggests not using log loss.

For further analysis, we must first define some function converting fw(x) into label predictions. The only reasonable approach is to threshold at 0.5. For log loss and squared loss, any other threshold is inconsistent. Since the optimal predictor for hinge loss always takes value 0 or 1, there is some freedom in how we convert, but a reasonable approach is to also threshold at 0.5.

Now, we want to analyze the stability of predictions. In other words, if an adversary picks the true conditional probability distribution p(y|x) and the prediction fw‘(x), how does the proxy loss of fw‘(x) bound the 0-1 loss? Since we imagine that the conditional distribution is noisy, it’s important to actually consider a regret: how well we do minus the loss of the best possible predictor.

For each of these losses, an optimal strategy of the adversary is to have p(y|x) take value 0.5 – eps and fw‘(x) = 0.5. The 0-1 regret induced is simply 2 eps, since the best possible predictor has error rate 0.5 – eps while the actual predictor has error rate 0.5 + eps. For hinge loss, the regret is eps and for squared loss the regret is eps2. Doing some algebra, this implies that 2 hinge_regret bounds 0-1 regret while 2 squared_regret0.5 bounds 0-1 regret. Since we are only interested in regrets less than 1, the square root is undesirable, and hinge loss is preferred, because a stronger convergence of squared loss is needed to achieve the same guarantee on 0-1 loss.

Can we improve on hinge loss? I don’t know any proxy loss which is quantitatively better, but generalizations exist. The regret of hinge loss is the same as for absolute value loss |y-fw‘(x)| since they are identical for 0,1 labels. One advantage of absolute value loss is that it has a known and sometimes useful semantics for values between 0 and 1: the optimal prediction is the median. This makes the work on quantile regression (Two Three) seem particularly relevant for machine learning.

11/16/2008

Observations on Linearity for Reductions to Regression

Tags: Machine Learning,Reductions jl@ 6:54 pm

Dean Foster and Daniel Hsu had a couple observations about reductions to regression that I wanted to share. This will make the most sense for people familiar with error correcting output codes (see the tutorial, page 11).

Many people are comfortable using linear regression in a one-against-all style, where you try to predict the probability of choice i vs other classes, yet they are not comfortable with more complex error correcting codes because they fear that they create harder problems. This fear turns out to be mathematically incoherent under a linear representation: comfort in the linear case should imply comfort with more complex codes.

In particular, If there exists a set of weight vectors wi such that P(i|x)= <wi,x>, then for any invertible error correcting output code C, there exists weight vectors wc which decode to perfectly predict the probability of each class. The proof is simple and constructive: the weight vector wc can be constructed according to the linear superposition of wi implied by the code, and invertibility implies that a correct encoding implies a correct decoding.

This observation extends to all-pairs like codes which compare subsets of choices to subsets of choices using “don’t cares”.

Using this observation, Daniel created a very short proof of the PECOC regret transform theorem (here, and Daniel’s updated version).

One further observation is that under ridge regression (a special case of linear regression), for any code, there exists a setting of parameters such that you might as well use one-against-all instead, because you get the same answer numerically. The implication is that the advantages of codes more complex than one-against-all is confined to other prediction methods.

7/26/2008

Compositional Machine Learning Algorithm Design

There were two papers at ICML presenting learning algorithms for a contextual bandit-style setting, where the loss for all labels is not known, but the loss for one label is known. (The first might require a exploration scavenging viewpoint to understand if the experimental assignment was nonrandom.) I strongly approve of these papers and further work in this setting and its variants, because I expect it to become more important than supervised learning. As a quick review, we are thinking about situations where repeatedly:

  1. The world reveals feature values (aka context information).
  2. A policy chooses an action.
  3. The world provides a reward.

Sometimes this is done in an online fashion where the policy can change based on immediate feedback and sometimes it’s done in a batch setting where many samples are collected before the policy can change. If you haven’t spent time thinking about the setting, you might want to because there are many natural applications.

I’m going to pick on the Banditron paper (second one), which attacks the special case of the contextual bandit setting where exactly one of the rewards is 1 and all other actions result in reward 0, and show that (a) similar performance is achievable via a simple combination of existing modular technologies and (b) superior performance is achievable by optimizing some existing modular technologies for the realizable case. This algorithm is the hardest of the two to compete with, because it explicitly deals with the explore/exploit tradeoff. Note that I’m definitely not trying to minimize the paper—there is analysis in that paper which remains interesting to me and isn’t covered by what follows. I am happy that it was published. The point of this post is showing that a modular approach to building learning algorithms is a strong contender when we encounter new learning problems.

Given the problem statement, my approach to solving the problem would be to compose some modular technologies I know.

  1. Perceptron learning algorithm We chose a Binary Perceptron as a classification algorithm. This choice is intrinsically motivated by the great computational performance of a Perceptron. It’s also the closest binary supervised learning algorithm to the Banditron, which eliminates a source of variation in comparison. We could have easily chosen a different base learning algorithm, and in many applications this is highly desirable.
  2. Offset Tree Reduction The offset tree is a newer machine learning reduction from the contextual bandit setting to the standard supervised learning setting. It more robustly transforms a supervised learner’s performance into good policy performance than any other reduction. The offset tree also has good computational properties, since it produces at most log2 k binary examples per train or test event, where k is the number of actions. In some sense it’s unfair to include the offset tree because it hasn’t yet been formally published. In another sense, that’s what this post is about.
  3. Epoch-Greedy exploration. The Epoch-greedy approach shows how to handle the explore/exploit tradeoff for learning in a contextual bandit setting as a function of a sample complexity bound. For common sample complexity bounds, we get an O(T2/3) online regret where T is the total number of timesteps.
  4. Occam’s Razor Bound The Occam’s Razor bound limits the regret of an empirical error minimizing perceptron as function of the number of examples. The bound (and it’s many cousins) are often loose, so the only thing we’ll really use is the denominator which says that regret scales as 1/sqrt(number of training examples) in the worst case. Applying Epoch-Greedy to the Occam’s Razor bound gives you an exploration probability of about C/t1/3 where t is the round number.

Each component above has been analyzed in isolation and is at least a reasonable approach (some are the best possible). Each of these components is also composable. Fitting these pieces together, we get an online learning algorithm (agnostic offset-tree perceptron) that chooses to explore uniformly at random amongst the actions with probability about 1/t1/3. How well does it perform? On the 4 class reuters based dataset used in the Banditron paper, we get the following accumulated average error rates with some code.:

The right plot is from the Banditron paper. The Perceptron line in both plots is for an algorithm which learns knowing the full loss function of each example, so it represents an ideal we don’t expect to achieve here. There are three results in the left plot:

  1. The blue line is a version of the component set where the Occam’s Razor bound and the Offset Tree reduction have been optimized for the realizable case. This was the first thing we tested (and it’s the result I mentioned at Shai‘s ICML talk). It turns out this approach works substantially better than the Banditron, achieving an error rate about halfway between the Banditron and the Multiclass Perceptron. The two components that we tweaked are:
    1. Realizable case Bound It’s well known that in the realizable case the regret of a chosen classifier should scale as 1/t rather than 1/t0.5. Plugging this into epoch-greedy, we get that the probability of exploration should be about 1/t0.5.
    2. Realizable case Offset Tree A basic observation is that in the realizable setting, every observation should create an example to tune the learning algorithm. In the context of the perceptron, this implies every error creates an example. The offset tree reduction can be altered to take this into account by eliminating the importance weight from all updates, and updating even for exploitation examples which are not drawn uniform randomly.
  2. The red line is what you get with exactly the component set stated above. We were curious about the degree to which a general purpose algorithm can perform well on this application as the realizable case algorithm is definitely broken when the problem is inherently noisy. The performance is somewhat worse than Banditron. I believe this is because it explores only about 1% of the time while the Banditron plot comes from exploring about 5% of the time.
  3. The green line is from a component set where epoch greedy and the offset tree have been tweaked to keep track of and use the distribution over actions at every timestep. This tweaks allows the amount of exploration as measured by the sum of importance weights of training examples to almost double. As we see, this approach improves performance, almost as if we doubled the number of examples, giving it similar performance to the Banditron. The tweaks used for the component set are:
    1. Stochastic Epoch-Greedy Instead of deterministically exploring every 1/(bound_gap) times, choose to explore with probability 1/(bound_gap), and pass this probability to the offset tree reduction.
    2. Nonuniform Offset Tree Tweak the Offset Tree in the obvious way to take into account nonuniform exploration. In particular, 1/2 is replaced with K/p(a) where p(a) is the probability of the action taken conditioned on one of two actions being taken. We set K so that this value is 1 when a nonexploit action is taken, which implies the importance weight is p(a)/(2-p(a)) when the exploit action is taken.
    3. Importance Weighted Perceptron We dealt with importance weights generated by the offset tree reduction by scaling any update by the importance weight.

People may be dissatisfied with the component assembly approach to learning algorithm design, because all of the pieces are not analyzed together. For example, the Banditron paper essentially proves that certain conditions are sufficient for the Banditron algorithm to perform well. This is a more complete guarantee than “all the pieces we stuck together are known to work well when analyzed in isolation”, but this style of guarantee has limitations which are both obvious and often overlooked. These guarantees do not show necessity of these conditions, and hence characterize only a subset of settings where the Banditron works. Unless you are lucky enough to know that your application satisfies the sufficient conditions, you’ll basically have to try the Banditron and see if it works for any particular application. Another drawback is that the complexity of analyzing all the different pieces simultaneously means that it’s difficult to use the best elements together. This last point is what leaves me dissatisfied with the complete analysis approach—it produces algorithms which are simple to analyze rather than optimized for performance.

If you are thinking “I have a better algorithm for solving one problem”, then you’ve missed the point of this post. The point of this post is that compositional design is good for solving many learning problems. This post is about one example of that approach in action. We start by assembling a set of components which we know work well from individual component analysis. Then, we try to optimize the performance of the assembly by swapping components or improving individual components to address known properties of the problem or observed deficiencies. In this particular case, we end up with a better performing algorithm and better components (such as stochastic epoch greedy) which are directly reusable in other settings. The essence of this approach is understanding that there is a real vocabulary of interchangeable components and actively using it in designing learning algorithms.

I would like to thank Alina who helped substantially with this post. I would also like to thank Shai for providing data and helping setup a clean comparison and Sham for helpful discussion.

11/28/2007

Computational Consequences of Classification

In the regression vs classification debate, I’m adding a new “pro” to classification. It seems there are computational shortcuts available for classification which simply aren’t available for regression. This arises in several situations.

  1. In active learning it is sometimes possible to find an e error classifier with just log(e) labeled samples. Only much more modest improvements appear to be achievable for squared loss regression. The essential reason is that the loss function on many examples is flat with respect to large variations in the parameter spaces of a learned classifier, which implies that many of these classifiers do not need to be considered. In contrast, for squared loss regression, most substantial variations in the parameter space influence the loss at most points.
  2. In budgeted learning, where there is either a computational time constraint or a feature cost constraint, a classifier can sometimes be learned to very high accuracy under the constraints while a squared loss regressor could not. For example, if there is one feature which determines whether a binary label has probability less than or greater than 0.5, a great classifier exists using just one feature. Because squared loss is sensitive to the exact probability, many more features may be required to learn well with respect to squared loss.

6/19/2007

How is Compressed Sensing going to change Machine Learning ?

Compressed Sensing (CS) is a new framework developed by Emmanuel Candes, Terry Tao and David Donoho. To summarize, if you acquire a signal in some basis that is incoherent with the basis in which you know the signal to be sparse in, it is very likely you will be able to reconstruct the signal from these incoherent projections.

Terry Tao, the recent Fields medalist, does a very nice job at explaining the framework here. He goes further in the theory description in this post where he mentions the central issue of the Uniform Uncertainty Principle. It so happens that random projections are on average incoherent, within the UUP meaning, with most known basis (sines, polynomials, splines, wavelets, curvelets …) and are therefore an ideal basis for Compressed Sensing. [ For more in-depth information on the subject, the Rice group has done a very good job at providing a central library of papers relevant to the growing subject: http://www.dsp.ece.rice.edu/cs/ ]

The Machine Learning community has looked at Random Projections before, for instance:

  • Experiments with Random Projections for Machine Learning by Fradkin and Madigan (KDD-03.)
  • Face Recognition Experiments with Random Projection by Goel, Bebis and Nefian
  • Dimensionality reduction by random mapping: Fast similarity computation for clustering by S. Kaski (Proceedings of IEEE International Joint Conference on Neural Networks, 1998.)
  • but while they seem to give somewhat comparable results with regards to PCA, the number of contributions on the subject does not seem overwhelming. Maybe one of the reason is that in most papers cited above, the main theoretical reason for using Random projections lies with the Johnson-Lindenstrauss (JL) lemma. As a consequence, most random matrices used in these publications come from the Database world and not from the newer framework of Compressed Sensing (a list of these matrices and their properties can be found in the middle of this page). The uncanny reliance on Random projections within the JL lemma and in the Compressed Sensing setting was explained by Richard Baraniuk, Mark Davenport, Ronald DeVore, and Michael Wakin in this paper entitled: A simple proof of the restricted isometry property for random matrices. However, the most interesting fallout from this comparison between JL and CS comes in the form of the contribution by Richard Baraniuk and Michael Wakin in Random projections of smooth manifolds. I’ll let the abstract speak for itself:

    We propose a new approach for nonadaptive dimensionality reduction of manifold-modeled data, demonstrating that a small number of random linear projections can preserve key information about a manifold-modeled signal……As our main theoretical contribution, we establish a sufficient number M of random projections to guarantee that, with high probability, all pairwise Euclidean and geodesic distances between points on M are well-preserved under the mapping \phi. Our results bear strong resemblance to the emerging theory of Compressed Sensing (CS), in which sparse signals can be recovered from small numbers of random linear measurements. As in CS, the random measurements we propose can be used to recover the original data in RN. Moreover, like the fundamental bound in CS, our requisite M is linear in the “information level” K and logarithmic in the ambient dimension N; we also identify a logarithmic dependence on the volume and curvature of the manifold. In addition to recovering faithful approximations to manifold-modeled signals, however, the random projections we propose can also be used to discern key properties about the manifold. We discuss connections and contrasts with existing techniques in manifold learning, a setting where dimensionality reducing mappings are typically nonlinear and constructed adaptively from a set of sampled training data.

    It looks as though, as a result, Universal Dimensionality Reduction is achieved by some properly chosen random projections. In the case of data living in a low dimensional manifold, the JL lemma states that the number of random projections is proportional to the number of points or samples from that manifold, on the other hand, CS seems to show that the number of random projections is proportional to the characteristic of the manifold only.

    The results highlighted by Wakin and Baraniuk are very compelling but there is another appealing reason to Random Projections: Robustness. While trying to mimick nature in the learning process, one cannot but be amazed at the reliability of the biological system. On the other hand, even the researchers that model these processes do not realize or point out that this robustness is in part due to random projections. Case in point, the excellent work of Thomas Serre, Aude Oliva and Tomaso Poggio culminating in a paper describing a biology inspired model of brain that shows its ability to process information in a feedforward fashion. The modeling is new in that, in this area of science, there is a central issue as to whether the brain works in a one-way process or with many loops. In the paper, the feature dimension reduction model (which is what this process is) uses random projections as I pointed out recently.

    Because of the intrinsic dimension reduction capability, Mike Wakin has also shown efficient nearest neighbor searches using few random projections (see figure 3 of this paper). I could go on but the point is that since CS is a revolution in the world of signal/feature acquisition and processing (see the analog-to-information A2I site ) one cannot but wonder aloud how this will affect Machine Learning in general.

    5/8/2007

    Conditional Tournaments for Multiclass to Binary

    This problem has been cracked (but not quite completely solved) by Alina, Pradeep, and I. The problem is essentially finding a better way to reduce multiclass classification to binary classification. The solution is to use a carefully crafted tournament, the simplest version of which is a single elimination tournament where the “players” are the different classes. An example of the structure is here:


    For the single elimination tournament, we can prove that:
    For all multiclass problems D, for all learned binary classifiers c, the regret of an induced multiclass classifier is bounded by the regret of the binary classifier times log2 k. Restated:

    regmulticlass(D,Filter_tree_test(c)) <= regbinary (Filter_tree_train(D),c)

    Here:

    1. Filter_tree_train(D) is the induced binary classification problem
    2. Filter_tree_test(c) is the induced multiclass classifier.
    3. regmulticlass is the multiclass regret (= difference between error rate and minimum possible error rate)
    4. regbinary is the binary regret

    This result has a slight dependence on k which we suspect is removable. The current conjecture is that this dependence can be removed by using higher order tournaments such as double elimination, triple elimination, up to log2 k-elimination.

    The key insight which makes the result possible is conditionally defining the prediction problems at interior nodes. In essence, we use the learned classifiers from the first level of the tree to filter the distribution over examples reaching the second level of the tree. This process repeats, until the root node is reached. Further details, including a more precise description and some experimental results are in the draft paper.

    3/15/2007

    Alternative Machine Learning Reductions Definitions

    A type of prediction problem is specified by the type of samples produced by a data source (Example: X x {0,1}, X x [0,1], X x {1,2,3,4,5}, etc…) and a loss function (0/1 loss, squared error loss, cost sensitive losses, etc…). For simplicity, we’ll assume that all losses have a minimum of zero.

    For this post, we can think of a learning reduction as

    1. A mapping R from samples of one type T (like multiclass classification) to another type T’ (like binary classification).
    2. A mapping Q from predictors for type T’ to predictors for type T.

    The simplest sort of learning reduction is a “loss reduction”. The idea in a loss reduction is to prove a statement of the form:
    Theorem For all base predictors b, for all distributions D over examples of type T:

    E(x,y) ~ D LT(y,Q(b,x)) <= f(E(x’,y’)~R(D) LT’(y’,b(x’)))

    Here LT is the loss for the type T problem and LT’ is the loss for the type T’ problem. Also, R(D) is the distribution over samples induced by first drawing from D and then mapping the sample via R. The function f() is the loss transform function—we try to find reductions R,Q which minimize it’s value.

    If R,Q are deterministic, then there always exists a choice of D,b such that the loss rate on the right hand side is 0. However, it’s common to encounter real-world learning problems D which are inherently noisy, implying that the induced problem D’ is often inherently noisy. Distinguishing between errors due to environmental noise and errors due to base predictor mistakes seems important (and experimentally, it has been). Regret transform reductions can get at this. They have theorems of the form:
    Theorem For all base predictors b, for all distributions D over examples of type T:

    E(x,y) ~ D LT(y,Q(b,x)) – minc E(x,y) ~ D LT(y,c(x)) <= f(E(x’,y’)~R(D) LT’(y’,b(x’)) – minb’ E(x’,y’)~R(D) LT’(y’,b’(x’)))

    The essential idea in regret transform reductions is that we subtract off the inherent noise in both the induced and original problem, and bound the excess loss due to suboptimal prediction directly.

    The skeletons of the theory for these families of reductions have been layed out at this point. There remain some open problems, but another interesting direction to consider is other families of reductions. The hope is that by placing more stringent requirements on reductions, we limit ourselves to algorithms which tend to perform better in practice. This hope is pretty reasonable—empirically, we have observed a consistent step up in performance going from loss transform to regret transform reductions.

    1. Limited Regret Transform Reductions. The fact that the minimum is taken over all predictors in regret transforms is counterintuitive to some people, who are used to “Empirical Risk Minimization” statements where a minimum is taken over a limited set of predictors. We could imagine theorem statements of the form:
      Theorem For all sets of base predictors B, For all base predictors b, for all distributions D over examples of type T:
      E(x,y) ~ D LT(y,Q(b,x)) – minb’ in B E(x,y) ~ D LT(y,Q(b’,x)) <= f(E(x’,y’)~R(D) LT’(y’,b(x’)) – minb’ in B E(x’,y’)~R(D) LT’(y’,b’(x’)))

      This is a more general statement than a regret transform reduction—when B is the set of all base predictors, we recover standard regret transforms
      One case where it’s easy to see that this kind of statement holds is for the reduction from importance weighted binary classification to binary classification. However, little more is currently known.
    2. Reversible Reductions. This is an idea which Russell Impagliazzo first mentioned to me. Essentially, we limit ourselves to reductions with the property that they are reversible. Reversibility can be tested by mapping from one problem to another, and then back. There are a several variant theorem statements we could imagine. The most tractable variant for analysis might be the following:
      Theorem There exists R-1,Q-1 such that for all base predictors b, for base learning problems D’:
      E(x’,y’)~D’ LT’(y’,b(x’)) = E(x’,y’) ~ R(R-1(D’)) LT’(y’,b(x’))

      and Q-1(Q(b))=b

      Closely related (but different) is the following:
      Theorem There exists R-1,Q-1 such that for all type T predictors h, for all type T distributions D:
      E(x,y) ~ D LT(y,h(x)) = E(x,y)~R-1(R(D)) LT(y,h(x))

      and Q(Q-1(h)) = h
    3. Bayesian Reductions This is an idea which Simon Osindero mentioned. The basic observation is that Bayes Law is pretty important to the process of learning. We would like it to be the case that Bayes Law and reductions compose. A theorem statement of the following form might be about right.
      Theorem For some large family of priors P over distributions D of type T:
      Bayes(P,(x,y)~D~P) = Q(Bayes(R(P),(x’,y’)~D’~R(P)))

      Here “Bayes” is a learning algorithm which takes as input a prior P (or R(P)), and a sample (x,y) drawn by first drawing a D from P and then drawing from D (and similarly for the induced problem). Also, R(P) is the prior induced by mapping D to R(D) after drawing from P.

    The two missing components for these kinds of reductions are:

    1. Theoretical evidence that we can satisfy these definitions of reduction between interesting types of learning problems.
    2. Empirical evidence that algorithmic modifications driven by the theory are useful.

    My experience is that analyzing reductions has yielded significant insight into how to solve learning problems, so I would encourage anyone with a bit of theoretical inclination in Machine Learning to consider the above (or other) families of reductions.

    3/3/2007

    All Models of Learning have Flaws

    Attempts to abstract and study machine learning are within some given framework or mathematical model. It turns out that all of these models are significantly flawed for the purpose of studying machine learning. I’ve created a table (below) outlining the major flaws in some common models of machine learning.

    The point here is not simply “woe unto us”. There are several implications which seem important.

    1. The multitude of models is a point of continuing confusion. It is common for people to learn about machine learning within one framework which often becomes there “home framework” through which they attempt to filter all machine learning. (Have you met people who can only think in terms of kernels? Only via Bayes Law? Only via PAC Learning?) Explicitly understanding the existence of these other frameworks can help resolve the confusion. This is particularly important when reviewing and particularly important for students.
    2. Algorithms which conform to multiple approaches can have substantial value. “I don’t really understand it yet, because I only understand it one way”. Reinterpretation alone is not the goal—we want algorithmic guidance.
    3. We need to remain constantly open to new mathematical models of machine learning. It’s common to forget the flaws of the model that you are most familiar with in evaluating other models while the flaws of new models get exaggerated. The best way to avoid this is simply education.
    4. The value of theory alone is more limited than many theoreticians may be aware. Theories need to be tested to see if they correctly predict the underlying phenomena.

    Here is a summary what is wrong with various frameworks for learning. To avoid being entirely negative, I added a column about what’s right as well.

    Name Methodology What’s right What’s wrong
    Bayesian Learning You specify a prior probability distribution over data-makers, P(datamaker) then use Bayes law to find a posterior P(datamaker|x). True Bayesians integrate over the posterior to make predictions while many simply use the world with largest posterior directly. Handles the small data limit. Very flexible. Interpolates to engineering.
    1. Information theoretically problematic. Explicitly specifying a reasonable prior is often hard.
    2. Computationally difficult problems are commonly encountered.
    3. Human intensive. Partly due to the difficulties above and partly because “first specify a prior” is built into framework this approach is not very automatable.
    Graphical/generative Models Sometimes Bayesian and sometimes not. Data-makers are typically assumed to be IID samples of fixed or varying length data. Data-makers are represented graphically with conditional independencies encoded in the graph. For some graphs, fast algorithms for making (or approximately making) predictions exist. Relative to pure Bayesian systems, this approach is sometimes computationally tractable. More importantly, the graph language is natural, which aids prior elicitation.
    1. Often (still) fails to fix problems with the Bayesian approach.
    2. In real world applications, true conditional independence is rare, and results degrade rapidly with systematic misspecification of conditional independence.
    Convex Loss Optimization Specify a loss function related to the world-imposed loss fucntion which is convex on some parametric predictive system. Optimize the parametric predictive system to find the global optima. Mathematically clean solutions where computational tractability is partly taken into account. Relatively automatable.
    1. The temptation to forget that the world imposes nonconvex loss functions is sometimes overwhelming, and the mismatch is always dangerous.
    2. Limited models. Although switching to a convex loss means that some optimizations become convex, optimization on representations which aren’t single layer linear combinations is often difficult.
    Gradient Descent Specify an architecture with free parameters and use gradient descent with respect to data to tune the parameters. Relatively computationally tractable due to (a) modularity of gradient descent (b) directly optimizing the quantity you want to predict.
    1. Finicky. There are issues with paremeter initialization, step size, and representation. It helps a great deal to have accumulated experience using this sort of system and there is little theoretical guidance.
    2. Overfitting is a significant issue.
    Kernel-based learning You chose a kernel K(x,x’) between datapoints that satisfies certain conditions, and then use it as a measure of similarity when learning. People often find the specification of a similarity function between objects a natural way to incorporate prior information for machine learning problems. Algorithms (like SVMs) for training are reasonably practical—O(n2) for instance. Specification of the kernel is not easy for some applications (this is another example of prior elicitation). O(n2) is not efficient enough when there is much data.
    Boosting You create a learning algorithm that may be imperfect but which has some predictive edge, then apply it repeatedly in various ways to make a final predictor. A focus on getting something that works quickly is natural. This approach is relatively automated and (hence) easy to apply for beginners. The boosting framework tells you nothing about how to build that initial algorithm. The weak learning assumption becomes violated at some point in the iterative process.
    Online Learning with Experts You make many base predictors and then a master algorithm automatically switches between the use of these predictors so as to minimize regret. This is an effective automated method to extract performance from a pool of predictors. Computational intractability can be a problem. This approach lives and dies on the effectiveness of the experts, but it provides little or no guidance in their construction.
    Learning Reductions You solve complex machine learning problems by reducing them to well-studied base problems in a robust manner. The reductions approach can yield highly automated learning algorithms. The existence of an algorithm satisfying reduction guarantees is not sufficient to guarantee success. Reductions tell you little or nothing about the design of the base learning algorithm.
    PAC Learning You assume that samples are drawn IID from an unknown distribution D. You think of learning as finding a near-best hypothesis amongst a given set of hypotheses in a computationally tractable manner. The focus on computation is pretty right-headed, because we are ultimately limited by what we can compute. There are not many substantial positive results, particularly when D is noisy. Data isn’t IID in practice anyways.
    Statistical Learning Theory You assume that samples are drawn IID from an unknown distribution D. You think of learning as figuring out the number of samples required to distinguish a near-best hypothesis from a set of hypotheses. There are substantially more positive results than for PAC Learning, and there are a few examples of practical algorithms directly motivated by this analysis. The data is not IID. Ignorance of computational difficulties often results in difficulty of application. More importantly, the bounds are often loose (sometimes to the point of vacuousness).
    Decision tree learning Learning is a process of cutting up the input space and assigning predictions to pieces of the space. Decision tree algorithms are well automated and can be quite fast. There are learning problems which can not be solved by decision trees, but which are solvable. It’s common to find that other approaches give you a bit more performance. A theoretical grounding for many choices in these algorithms is lacking.
    Algorithmic complexity Learning is about finding a program which correctly predicts the outputs given the inputs. Any reasonable problem is learnable with a number of samples related to the description length of the program. The theory literally suggests solving halting problems to solve machine learning.
    RL, MDP learning Learning is about finding and acting according to a near optimal policy in an unknown Markov Decision Process. We can learn and act with an amount of summed regret related to O(SA) where S is the number of states and A is the number of actions per state. Has anyone counted the number of states in real world problems? We can’t afford to wait that long. Discretizing the states creates a POMDP (see below). In the real world, we often have to deal with a POMDP anyways.
    RL, POMDP learning Learning is about finding and acting according to a near optimaly policy in a Partially Observed Markov Decision Process In a sense, we’ve made no assumptions, so algorithms have wide applicability. All known algorithms scale badly with the number of hidden states.

    This set is incomplete of course, but it forms a starting point for understanding what’s out there. (Please fill in the what/pro/con of anything I missed.)

    10/8/2006

    Incompatibilities between classical confidence intervals and learning.

    Classical confidence intervals satisfy a theorem of the form: For some data sources D,

    PrS ~ D(f(D) > g(S)) > 1-d

    where f is some function of the distribution (such as the mean) and g is some function of the observed sample S. The constraints on D can vary between “Independent and identically distributed (IID) samples from a gaussian with an unknown mean” to “IID samples from an arbitrary distribution D“. There are even some confidence intervals which do not require IID samples.

    Classical confidence intervals often confuse people. They do not say “with high probability, for my observed sample, the bounds holds”. Instead, they tell you that if you reason according to the confidence interval in the future (and the constraints on D are satisfied), then you are not often wrong. Restated, they tell you something about what a safe procedure is in a stochastic world where d is the safety parameter.

    There are a number of results in theoretical machine learning which use confidence intervals. For example,

    1. The E3 algorithm uses confidence intervals to learn a near optimal policy for any MDP with high probability.
    2. Set Covering Machines minimize a confidence interval upper bound on the true error rate of a learned classifier.
    3. The A2 uses confidence intervals to safely deal with arbitrary noise while taking advantage of active learning.

    Suppose that we want to generalize thse algorithms in a reductive style. The goal would be to train a regressor to predict the output of g(S) for new situations. For example, a good regression prediction of g(S) might allow E3 to be applied to much larger state spaces. Unfortunately, this approach seems to fail badly due to a mismatch between the semantics of learning and the semantics of a classical confidence interval.

    1. It’s difficult to imagine a constructive sampling mechanism. In a large state space, we may never encounter the same state twice, so we can not form meaningful examples of the form “for this state-action, the correct confidence interval is y“.
    2. When we think of succesful learning, we typically think of it in an l1 sense—the expected error rate over the data generating distribution is small. Confidence intervals have a much stronger meaning as we would like to apply them: with high probability, in all applications, the confidence interval holds. This mismatch appears unaddressable.

    It is tempting to start plugging in other notions such as Bayesian confidence intervals or quantile regression systems. Making these approaches work at a theoretical level on even simple systems is an open problem, but there is plenty of motivation to do so.

    9/9/2006

    How to solve an NP hard problem in quadratic time

    This title is a lie, but it is a special lie which has a bit of truth.

    If n players each play each other, you have a tournament. How do you order the players from weakest to strongest?

    The standard first attempt is “find the ordering which agrees with the tournament on as many player pairs as possible”. This is called the “minimum feedback arcset” problem in the CS theory literature and it is a well known NP-hard problem. A basic guarantee holds for the solution to this problem: if there is some “true” intrinsic ordering, and the outcome of the tournament disagrees k times (due to noise for instance), then the output ordering will disagree with the original ordering on at most 2k edges (and no solution can be better).

    One standard approach to tractably solving an NP-hard problem is to find another algorithm with an approximation guarantee. For example, Don Coppersmith, Lisa Fleischer and Atri Rudra proved that ordering players according to the number of wins is a 5-approximation to the NP-hard problem.

    An even better approach is to realize that the NP hard problem may not be the real problem. The real problem may be finding a good approximation to the “true” intrinsic ordering given noisy tournament information.

    In a learning setting, the simplest form of ranking problem is “bipartite ranking” where every element has a value of either 0 or 1 and we want to order 0s before 1s. A common way to measure the performance of bipartite ranking is according to “area under the ROC curve” (AUC) = 1 – the fraction of out-of-order pairs. Nina, Alina, Greg and I proved that if we learn a comparison function which errs on k dissimilar pairs, then ordering according to the number of wins yields an order within 4k edge reversals of the original ordering. As a reduction statement(*), this shows that an error rate of e for a learned pairwise binary classifier produces an ordering with an expected AUC of 1 – 4e. The same inequality even holds for a (stronger) regret transform. If r = e – emin is the regret of the binary pairwise classifier, then the AUC regret is bounded by 4r. (Here emin is the error rate of the best possible classifier which predicts knowing the most likely outcome.) The regret result extends to more general measures of ordering than simply AUC.

    We were unable to find any examples where ordering according to the degree produced more than a 2r AUC regret. Nikhil Bansal, Don, and Greg have worked out a tightened proof which gives exactly this upper bound. At the end of the day, we have an algorithm with satisfies precisely the same guarantee as the NP hard solution.

    There are two lessons here. The learning lesson is that a good pairwise comparator implies the ability to rank well according to AUC. The general research lesson is that an NP hard problem for an approximate solution is not an intrinsic obstacle. Sometimes there exist simple tractable algorithms which satisfy the same guarantees as the NP hard solution.

    (*) To prove the reduction, you must make sure that your form pairwise examples in the right way. Your source of pairwise ordering examples must be uniform over the dissimilar pairs containing one example with label 1 and one example with label 0.

    7/13/2006

    Regression vs. Classification as a Primitive

    Tags: Machine Learning,Reductions jl@ 7:53 pm

    For learning reductions we have been concentrating on reducing various complex learning problems to binary classification. This choice needs to be actively questioned, because it was not carefully considered.

    Binary clasification is learning a classifier c:X -> {0,1} so as to minimize the probability of being wrong, Prx,y~D(c(x) <> y).

    The primary alternative candidate seems to be squared error regression. In squared error regression, you learn a regressor s:X -> [0,1] so as to minimize squared error, Ex,y~D (s(x)-y)2.

    It is difficult to judge one primitive against another. The judgement must at least partially be made on nontheoretical grounds because (essentially) we are evaluating a choice between two axioms/assumptions.

    These two primitives are significantly related. Classification can be reduced to regression in the obvious way: you use the regressor to predict D(y=1|x), then threshold at 0.5. For this simple reduction a squared error regret of r implies a classification regret of at most r0.5. Regression can also be used to reduce to classification using the Probing algorithm. (This is much more obvious when you look at an updated proof. ) Under this reduction, a classification regret of r implies a squared error regression regret of at most r.

    Both primitives enjoy a significant amount of prior work with (perhaps) classification enjoying more work in the machine learning community and regression having more emphasis in the statistics community.

    The (nonhistoric) reasons for preferring classification seem to be:

    1. Aesthetically, what could be a more natural primitive than predicting a single bit?
    2. Classification is (potentially) inherently more representationally concise. When translated into transistors, a classification algorithm might use fewer transistors than a regressor, simply because the natural representation is bits rather than reals (~= floats).

    There are several reasons to consider regression over classification:

    1. More uniform convergence. For a single squared error regressor, the rate of convergence of the estimate of squared error to the expected squared error goes as 1/m, assuming IID samples. For a single binary classifier, the rate of convergence of the estimate of binary loss to the expected binary loss goes as a function varying between 1/m and 1/m0.5.
    2. There is a computational inequivalence between the primitives, as far as we know. In particular, the Probing algorithm must call a classifier several times in several ways to make a high precision regression prediction. On the other hand, classification via regression requires one call to the underlying regressor.
    3. Squared error regression learning is often easier than 0/1 classification learning. This is becaue squared error regression is convex, but 0/1 loss is not. Note: this does not imply that squared error regression is convex (It isn’t for general regression algorithms). Instead, it just means that nonconvexity is not enforced by the loss function.

    The mathematical evidence points toward squared error regression as a better primitive, although doubts still seem reasonable to entertain.

    5/23/2006

    What is the best regret transform reduction from multiclass to binary?

    This post is about an open problem in learning reductions.

    Background A reduction might transform a a multiclass prediction problem where there are k possible labels into a binary learning problem where there are only 2 possible labels. On this induced binary problem we might learn a binary classifier with some error rate e. After subtracting the minimum possible (Bayes) error rate b, we get a regret r = e – b. The PECOC(Probabilistic Error Correcting Output Code) reduction has the property that binary regret r implies multiclass regret at most 4r0.5.

    The problem This is not the “rightest” answer. Consider the k=2 case, where we reduce binary to binary. There exists a reduction (the identity) with the property that regret r implies regret r. This is substantially superior to the transform given by the PECOC reduction, which suggests that a better reduction may exist for general k. For example, we can not rule out the possibility that a reduction R exists with regret transform guaranteeing binary regret r implies at most multiclass regret c(k) r where c(k) is a k dependent constant between 1 and 4.

    Difficulty I believe this is a solvable problem, given some serious thought.

    Impact The use of some reduction from multiclass to binary is common practice, so a good solution could be widely useful. One thing to be aware of is that there is a common and reasonable concern about the ‘naturalness’ of induced problems. There seems to be no way to address this concern other than via empirical testing. On the theoretical side, a better reduction may help us understand whether classification or l2 regression is the more natural primitive for reduction. The PECOC reduction essentially first turns a binary classifier into an l2 regressor and then uses the regressor repeatedly to make multiclass predictions.

    Some background material which may help:

    1. Dietterich and Bakiri introduce Error Correcting Output Codes.
    2. Guruswami and Sahai analyze ECOC as an error transform reduction. (see lemma 2)
    3. Allwein, Schapire, and Singer generalize ECOC to use loss-based decoding.
    4. Beygelzimer and Langford showed that ECOC is not a regret transform and proved the PECOC regret transform.

    5/16/2006

    The value of the orthodox view of Boosting

    The term “boosting” comes from the idea of using a meta-algorithm which takes “weak” learners (that may be able to only barely predict slightly better than random) and turn them into strongly capable learners (which predict very well). Adaboost in 1995 was the first widely used (and useful) boosting algorithm, although there were theoretical boosting algorithms floating around since 1990 (see the bottom of this page).

    Since then, many different interpretations of why boosting works have arisen. There is significant discussion about these different views in the annals of statistics, including a response by Yoav Freund and Robert Schapire.

    I believe there is a great deal of value to be found in the original view of boosting (meta-algorithm for creating a strong learner from a weak learner). This is not a claim that one particular viewpoint obviates the value of all others, but rather that no other viewpoint seems to really capture important properties.

    Comparing with all other views of boosting is too clumsy, so I will pick one: “boosting coordinate-wise gradient descent (CWGD for short) on an exponential loss function” which started here and compare it with Adaboost.

    There are two advantages of the “weak to strong learning” view:

    1. Automatic computational speedups. In the “weak to strong learning” view, you automatically think about using a learning algorithm as a subroutine. As a consequence, we know the computation can be quite fast. In the CWGD view, using C4.5 (or some other algorithm) to pick the coordinate is an unmotivated decision. The straightforward thing to do is simply check each coordinate in turn which yields no computational speedups.
    2. Meta-algorithm based performance gains. Using a nontrivial base learning algorithm seems to improve performance. This is unsurprising—simply consider the limit where only one round of boosting is done. This is not well-accounted for by the CWGD view.

    The point here is not that the old view subsumes the CWGD view, but rather that the CWGD view does not account for all the value in the old view. In particular, the CWGD view suggests that a broader family of algorithms may be useful than the weak-to-strong view might suggest.

    This might seem like a “too meta” discussion, but it is very relevant to the process of research. We as researchers in machine learning have a choice of many methods of thinking about developing algorithms. Some methods are harder to use than others, so it is useful to think about what works well. Gradient descent is a core algorithmic tool in machine learning. After making a sequence of more-or-less unmotivated steps, we can derive Adaboost (and other algorithms) as an application of gradient descent. Or, we can study the notion of boosting weak learning to achieve strong learning and derive Adaboost. My impression is that the “weak learning to achieve strong learning” view is significantly more difficult to master than gradient descent, but it is also a significantly more precise mechanism for deriving useful algorithms. There are many gradient descent algorithms which are not very useful in machine learning. Amongst other things, the “weak to strong” view significantly informed some of the early development of learning reductions. It is no coincidence that Adaboost can be understood in this framework.

    3/17/2006

    Multitask learning is Black-Boxable

    Multitask learning is the problem of jointly predicting multiple labels simultaneously with one system. A basic question is whether or not multitask learning can be decomposed into one (or more) single prediction problems. It seems the answer to this is “yes”, in a fairly straightforward manner.

    The basic idea is that a controlled input feature is equivalent to an extra output. Suppose we have some process generating examples: (x,y1,y2) in S where y1 and y2 are labels for two different tasks. Then, we could reprocess the data to the form Sb(S) = {((x,i),yi): (x,y1,y2) in S, i in {1,2}} and then learn a classifier c:X x {1,2} -> Y. Note that (x,i) is the (composite) input. At testing time, given an input x, we can query c for the predicted values of y1 and y2 using (x,1) and (x,2).

    A strong form of equivalence can be stated between these tasks. In particular, suppose we have a multitask learning algorithm ML which learns a multitask predictor m:X -> Y x Y. Then the following theorem can be proved:

    For all ML for all S, there exists an inverse reduction Sm such that ML(S) = ML(Sm(Sb(S)).

    In other words, no information is lost in the transformation Sb which means everything which was learnable previously remains learnable.

    This may not be the final answer to the question because there may be some algorithm-dependent (mis)behavior associated with controlled feature i. It may also be the case that single task classification is computationally distinguishable from multitask classification. Certainly, computational concerns are one of the reasons specialized multitask classification algorithms exist.

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