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Active learning

Often, unlabeled data is easy to come by but labels are expensive. For instance, if you’re building a speech recognizer, it’s easy enough to get raw speech samples — just walk around with a microphone — but labeling even one of these samples is a tedious process in which a human must examine the speech signal and carefully segment it into phonemes. In the field of active learning, the goal is as usual to construct an accurate classifier, but the labels of the data points are initially hidden and there is a charge for each label you want revealed. The hope is that by intelligent adaptive querying, you can get away with significantly fewer labels than you would need in a regular supervised learning framework.

Here’s an example. Suppose the data lie on the real line, and the classifiers are simple thresholding functions, H = {hw}:

hw(x) = 1 if x > w, and 0 otherwise.

VC theory tells us that if the underlying distribution P can be classified perfectly by some hypothesis in H (called the realizable case), then in order to get a classifier with error rate at most e, it is enough to draw m = O(1/e) random labeled examples from P, and to return any classifier consistent with them. But suppose we instead draw m unlabeled samples from P. If we lay these points down on the line, their hidden labels are a sequence of 0’s followed by a sequence of 1’s, and the goal is to discover the point w at which the transition occurs. This can be accomplished with a simple binary search which asks for just log m labels. Thus active learning gives us an exponential improvement in the number of labels needed: by adaptively querying log m labels, we can automatically infer the rest of them.

Unfortunately, not all that much is known beyond this toy example. To date, the single main theoretical result in the field is [FSST97]‘s analysis of the query-by-committee (QBC) learning algorithm. In their model, the learner observes a stream of unlabeled data and makes spot decisions about whether or not to ask for a point’s label. They show that if the data is drawn uniformly from the surface of the d-dimensional unit sphere, and the hidden labels correspond perfectly to a homogeneous (i.e., through the origin) linear separator from this same distribution, then it is possible to achieve generalization error e after seeing O(d/e) points and requesting just O(d log 1/e) labels: an exponential improvement over the usual O(d/e) sample complexity of learning linear separators in a supervised setting. This remarkable result is tempered somewhat by the complexity of the QBC algorithm, which involves computing volumes of intermediate version spaces.

Some recent progress on active learning: [DKM05] show how a simple variant of the perceptron update can be used to achieve these same sample complexity bounds, in the same model. [D04] shows a variety of upper and lower bounds for active learning — for instance, if you allow linear separators which are non-homogeneous then in the above model the sample complexity necessarily shoots up to 1/e.

The theoretical terrain of active learning remains something of an unexplored wilderness. There has, however, been a lot of beautiful theory work (see [A02] for a roundup) on a related model in which the learner is allowed to synthesize query points, rather than simply choosing them from the pool of unlabeled data. This ran into some practical problems: [BL92] found that the resulting synthetic instances were often very difficult for a human to classify!

[A02] D. Angluin. Queries revisited.
[BL92] E. Baum and K. Lang. Query learning can work poorly when a human oracle is used.
[D04] S. Dasgupta. Analysis of a greedy active learning strategy.
[DKM05] S. Dasgupta, A. Kalai, and C. Monteleoni. Analysis of perceptron-based active learning.
[FSST97] Y. Freund, S. Seung, E. Shamir, and N. Tishby. Selective sampling using the query-by-committee algorithm.

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The State of Tight Bounds

What? Bounds are mathematical formulas relating observations to future error rates assuming that data is drawn independently. In classical statistics, they are calld confidence intervals.
Why?

  1. Good Judgement. In many applications of learning, it is desirable to know how well the learned predictor works in the future. This helps you decide if the problem is solved or not.
  2. Learning Essence. The form of some of these bounds helps you understand what the essence of learning is.
  3. Algorithm Design. Some of these bounds suggest, motivate, or even directly imply learning algorithms.

What We Know Now

There are several families of bounds, based on how information is used.

  1. Testing Bounds. These are methods which use labeled data not used in training to estimate the future error rate. Examples include the test set bound, progressive validation also here and here, train and test bounds, and cross-validation (but see the big open problem). These techniques are the best available for goal (1) above, but provide little information towards goals (2) or (3). Some of these techniques are computationally efficient while others are not.
  2. Unlabeled test set bounds Instead of using labeled data to construct a tight bound, it is sometimes possible to use unlabeled data.
  3. Training Bounds These are methods which use labeled data to for both training and testing. These bounds provide insight into goals (2) and (3), but are not very effective for goal (1) above. These bounds teach us about how many independent examples are required to guarantee learning success on different on different representations. Any bound of this sort implies a learning algorithm: “minimize the bound”. The set covering machine is an application of this approach to a (variant) sample compression bound. Most other potential applications aren’t “quite there” yet, although they are close. Here is a list of learning algorithms and bounds that “almost work” for these algorithms.
    1. SVM PAC-Bayes margin bound.
    2. Perceptron PAC-Bayes margin bound, Sample Compression Bound
    3. Neural Network PAC-Bayes bound.
    4. Decision Tree Occam’s razor bound.
    5. Decision Tree Pruning Rademacher Complexity Bounds
    6. Semisupervised Clustering PAC-MDL or transductive PAC-Bayes bound
    7. Nearest neighbor ??(Note that the cross validation approaches work well here.)

Limitations
The independence assumption is a significant limitation in the applicability of this approach since we often can not believe that independence is satisfied on natural learning problems. Some work has gone into weakening this assumption.

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The Big O and Constants in Learning

The notation g(n) = O(f(n)) means that in the limit as n approaches infinity there exists a constant C such that the g(n) is less than Cf(n).

In learning theory, there are many statements about learning algorithms of the form “under assumptions x,y, and z, the classifier learned has an error rate of at most O(f(m))“.

There is one very good reason to use O(): it helps you understand the big picture and neglect the minor details which are not important in the big picture. However, there are some important reasons not to do this as well.

  1. Unspeedup In algorithm analysis, the use of O() for time complexity is pervasive and well-justified. Determining the exact value of C is inherently computer architecture dependent. (The “C” for x86 processors might differ from the “C” on PowerPC processors.) Since many learning theorists come from a CS theory background, the O() notation is applied to generalization error. The O() abstraction breaks here—you can not generally change learning algorithm and decrease your error rate by some constant factor.
  2. You’re fired When you go and run a learning algorithm to acquire some predictor, the performance it achieves is a key quantity of significant interest. Using an algorithm with merely twice the error rate of a better algorithm can easily make the difference between “it works” and “it doesn’t”. This is not often true when programming, as evidenced by the large number of people who use computationally inefficient languages to solve problems.

We can’t say “never use O()“, because sometimes abstracting away details is the right thing to do. However, any use of O() should be analyzed for “reasonableness” more thoroughly than for computational complexity. Similarly, more interest should be allocated to improving constants in such analysis than is done in algorithms.

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Prior, “Prior” and Bias

Many different ways of reasoning about learning exist, and many of these suggest that some method of saying “I prefer this predictor to that predictor” is useful and necessary. Examples include Bayesian reasoning, prediction bounds, and online learning. One difficulty which arises is that the manner and meaning of saying “I prefer this predictor to that predictor” differs.

  1. Prior (Bayesian) A prior is a probability distribution over a set of distributions which expresses a belief in the probability that some distribution is the distribution generating the data.
  2. “Prior” (Prediction bounds & online learning) The “prior” is a measure over a set of classifiers which expresses the degree to which you hope the classifier will predict well.
  3. Bias (Regularization, Early termination of neural network training, etc…) The bias is some (often implicitly specified by an algorithm) way of preferring one predictor to another.

This only scratches the surface—there are yet more subtleties. For example the (as mentioned in meaning of probability) shifts from one viewpoint to another.

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Problem: Cross Validation

The essential problem here is the large gap between experimental observation and theoretical understanding.

Method K-fold cross validation is a commonly used technique which takes a set of m examples and partitions them into K sets (“folds”) of size m/K. For each fold, a classifier is trained on the other folds and then test on the fold.

Problem Assume only independent samples. Derive a classifier from the K classifiers with a small bound on the true error rate.

Past Work (I’ll add more as I remember/learn.)

  1. Devroye, Rogers, and Wagner analyzed cross validation and found algorithm specific bounds. Not all of this is online, but here is one paper.
  2. Michael Kearns and Dana Ron analyzed cross validation and found that under additional stability assumptions the bound for the classifier which learns on all the data is not much worse than for a test set of size m/K.
  3. Avrim Blum, Adam Kalai, and myself analyzed cross validation and found that you can do at least as well as a test set of size m/K with no additional assumptions using the randomized classifier which draws uniformly from the set of size K.
  4. Yoshua Bengio and Yves Grandvalet analyzed cross validation and concluded that there was no unbiased estimator of variance.
  5. Matti Kääriäinen noted that you can safely derandomize a stochastic classifier (such as one that randomizes over the K folds) using unlabeled data without additional assumptions.

Some Extreme Cases to Sharpen Intuition

  1. Suppose on every fold the learned classifier is the same. Then, the cross-validation error should behave something like a test set of size m. This is radically superior to a test set of size m/K.
  2. Consider leave-one-out cross validation. Suppose we have a “learning” algorithm that uses the classification rule “always predict the parity of the labels on the training set”. Suppose the learning problem is defined by a distribution which picks y=1 with probability 0.5. Then, with probability 0.5, all leave-one-out errors will be 0 and otherwise 1 (like a single coin flip).

(some discussion is here)

Difficulty I consider this a hard problem. I’ve worked on it without success and it’s an obvious problem (due to the pervasive use of cross validation) that I suspect other people have considered. Analyzing the dependency structure of cross validation is quite difficult.

Impact On any individual problem, solving this might have only have a small impact due to slightly improved judgement of success. But, because cross validation is used extensively, the overall impact of a good solution might be very significant.