Machine Learning (Theory)


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 Please join us. The subjects we plan to cover are essentially the keys to the kingdom of solving shallow interactive learning problems.

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