Machine Learning (Theory)

9/28/2011

Somebody’s Eating Your Lunch

Tags: CS,Online,Teaching jl@ 1:36 pm

Since we last discussed the other online learning, Stanford has very visibly started pushing mass teaching in AI, Machine Learning, and Databases. In retrospect, it’s not too surprising that the next step up in serious online teaching experiments are occurring at the computer science department of a university embedded in the land of startups. Numbers on the order of 100000 are quite significant—similar in scale to the number of computer science undergraduate students/year in the US. Although these populations surely differ, the fact that they could overlap is worth considering for the future.

It’s too soon to say how successful these classes will be and there are many easy criticisms to make:

  1. Registration != Learning … but if only 1/10th complete these classes, the scale of teaching still surpasses the scale of any traditional process.
  2. 1st year excitement != nth year routine … but if only 1/10th take future classes, the scale of teaching still surpasses the scale of any traditional process.
  3. Hello, cheating … but teaching is much harder than testing in general, and we already have recognized systems for mass testing.
  4. Online misses out … sure, but for students not enrolled in a high quality university program, this is simply not a relevant comparison. There are also benefits to being online as well, as your time might be better focused. Anecdotally, at Caltech, they let us take two classes at the same time, which I did a few times. Typically, I had a better grade in the class that I skipped as the instructor had to go through things rather slowly.
  5. Where’s the beef? The hard nosed will want to know how to make money, which is always a concern. But, a decent expectation is that if you first figure out how to create value, you’ll find some way to make money. And, if you first wait until it’s clear how to make money, you won’t make any.

My belief is that this project will pan out, with allowances for the expected inevitable adjustments that you learn to make from experience. I think the critics miss an understanding of what’s possible and what motivates people.

The prospect of teaching 1 student means you might review some notes. The prospect of teaching ~10 students means you prepare some slides. The prospect of teaching ~100 students means you polish your slides well, trying to anticipate questions, and hopefully drawing on experience from previous presentations. I’ve never directly taught ~1000 students, but at that scale you must try very hard to make the presentation perfect, including serious testing with dry runs. 105 students must make getting out of bed in the morning quite easy.

Stanford has a significant first-mover advantage amongst top research universities, but it’s easy to imagine a few other (but not many) universities operating at a similar scale. Those that have the foresight to start a serious online teaching program soon will have a chance of being among the few. For other research universities, we can expect boutique traditional classes to continue for some time. These boutique classes may have some significant social value, because it’s easy to imagine that the few megaclasses miss important things in developing research areas. And for everyone working at teaching universities, someone is eating your lunch.

(Cross posted at CACM.)

9/21/2010

Regretting the dead

Nikos pointed out this new york times article about poor clinical design killing people. For those of us who study learning from exploration information this is a reminder that low regret algorithms are particularly important, as regret in clinical trials is measured by patient deaths.

Two obvious improvements on the experimental design are:

  1. With reasonable record keeping of existing outcomes for the standard treatments, there is no need to explicitly assign people to a control group with the standard treatment, as that approach is effectively explored with great certainty. Asserting otherwise would imply that the nature of effective treatments for cancer has changed between now and a year ago, which denies the value of any clinical trial.
  2. An optimal experimental design will smoothly phase between exploration and exploitation as evidence for a new treatment shows that it can be effective. This is old tech, for example in the EXP3.P algorithm (page 12 aka 59) although I prefer the generalized and somewhat clearer analysis of EXP4.P.

Done the right way, the clinical trial for a successful treatment would start with some initial small pool (equivalent to “phase 1” in the article) and then simply expanded the pool of participants over time as it proved superior to the existing treatment, until the pool is everyone. And as a bonus, you can even compete with policies on treatments rather than raw treatments (i.e. personalized medicine).

Getting from here to there seems difficult. It’s been 15 years since EXP3.P was first published, and the progress in clinical trial design seems glacial to us outsiders. Partly, I think this is a communication and education failure, but partly, it’s also a failure of imagination within our own field. When we design algorithms, we often don’t think about all the applications, where a little massaging of the design in obvious-to-us ways so as to suit these applications would go a long ways. Getting this right here has a substantial moral aspect, potentially saving millions of lives over time through more precise and fast deployments of new treatments.

6/13/2010

The Good News on Exploration and Learning

Consider the contextual bandit setting where, repeatedly:

  1. A context x is observed.
  2. An action a is taken given the context x.
  3. A reward r is observed, dependent on x and a.

Where the goal of a learning agent is to find a policy for step 2 achieving a large expected reward.

This setting is of obvious importance, because in the real world we typically make decisions based on some set of information and then get feedback only about the single action taken. It also fundamentally differs from supervised learning settings because knowing the value of one action is not equivalent to knowing the value of all actions.

A decade ago the best machine learning techniques for this setting where implausibly inefficient. Dean Foster once told me he thought the area was a research sinkhole with little progress to be expected. Now we are on the verge of being able to routinely attack these problems, in almost exactly the same sense that we routinely attack bread and butter supervised learning problems. Just as for supervised learning, we know how to create and reuse datasets, how to benchmark algorithms, how to reuse existing supervised learning algorithms in this setting, and how to achieve optimal rates of learning quantitatively similar to supervised learning.

This is also one of the times when understanding the basic theory can make a huge difference in your success. There are many wrong ways to attack contextual bandit problems or prepare datasets, and taking a wrong turn can easily mean the difference between failure and success. Understanding how contextual bandit problems differ from basic supervised learning problems is critical to routine success here.

All of the above is not meant to claim that everything is done research-wise here so we’ll try to outline where the current boundary of research lies as best we can. However, we are surely at a point both in terms of application demand (especially for internet applications of search, advertising, page optimization, but also medical applications and surely others) and methodology supply (with basic reliable techniques now easily available or created) where these techniques are shifting from theory esoterica to required education.

Given the above, Alina and I decided to prepare a tutorial to be given at Yahoo! Labs summer school (my first India trip!), ICML, KDD, and hopefully videolectures.net. Please join us. The subjects we plan to cover are essentially the keys to the kingdom of solving shallow interactive learning problems.

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.

12/7/2009

Vowpal Wabbit version 4.0, and a NIPS heresy

Tags: Code,Machine Learning,Online jl@ 12:42 pm

I’m releasing version 4.0(tarball) of Vowpal Wabbit. The biggest change (by far) in this release is experimental support for cluster parallelism, with notable help from Daniel Hsu.

I also took advantage of the major version number to introduce some incompatible changes, including switching to murmurhash 2, and other alterations to cachefiles. You’ll need to delete and regenerate them. In addition, the precise specification for a “tag” (i.e. string that can be used to identify an example) changed—you can’t have a space between the tag and the ‘|’ at the beginning of the feature namespace.

And, of course, we made it faster.

For the future, I put up my todo list outlining the major future improvements I want to see in the code. I’m planning to discuss the current mechanism and results of the cluster parallel implementation at the large scale machine learning workshop at NIPS later this week. Several people have asked me to do a tutorial/walkthrough of VW, which is arranged for friday 2pm in the workshop room—no skiing for me Friday. Come join us if this heresy interests you as well :)

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