In my previous post, I wrote about why I find learning theory to be a worthwhile endeavor. In this post I want to discuss a few open/under-explored areas in learning theory that I think are particularly interesting. If you know of progress in these areas, please email me or post in the comments. Continue reading “Learning Theory: What Next?”

# Author: Jonathan Huggins

## Learning Theory: Purely Theoretical?

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What’s learning theory good for, anyway? As I mentioned in my earlier blog post, not infrequently get into conversations with people in machine learning and related fields who don’t see the benefit of learning theory (that is, theory of learning). While that post offered one specific piece of evidence of how work seemingly only relevant in pure theory could lead to practical algorithms, I thought I would talk in more general terms why I see learning theory as a worthwhile endeavor.

There are two main flavors of learning theory, statistical learning theory (StatLT) and computational learning (CompLT). StatLT originated with Vladimir Vapnik, while the canonical example of CompLT, PAC learning, was formulated by Leslie Valiant. StatLT, in line with its “statistical” descriptor, focuses on asymptotic questions (though generally based on useful non-asymptotic bounds). It is less concerned with computational efficiency, which is where CompLT comes in. Computer scientists are all about efficient algorithms (which for the purposes of theory essentially means polynomial vs. super-polynomial time). Generally, StatLT results apply to a wider variety of hypothesis classes, with few or no assumptions made about the concept class (a concept class refers to the class of functions to which the data generating mechanism belongs). CompLT results apply to very specific concept classes but have stronger performance guarantees, often using polynomial time algorithms. I’ll do my best to defend both flavors, while also mentioning some of their limitations.

## Complexity of Inference in Bayesian Networks

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Developing efficient (i.e. polynomial time) algorithms with guaranteed performance is a central goal in computer science (perhaps *the* central goal). In machine learning, inference algorithms meeting these requirements are much rarer than we would like: often, an algorithm is either efficient but doesn’t perform optimally or vice versa. A number of results from the 1990’s demonstrate the challenges of, but also the potential for, efficient Bayesian inference. These results were carried out in the context of Bayesian networks.

Continue reading “Complexity of Inference in Bayesian Networks”

## Turning Theory into Algorithms

[latexpage] Some of the common complaints I hear about (learning) theoretical work run along the lines of “those bounds are meaningless in practice,” “that result doesn’t apply to any algorithm someone would actually use,” and “you lost me as soon as martingales/Banach spaces/measure-theoretic niceties/… got involved.” I don’t have a good answer for the latter concern, but a very nice paper by Sasha Rakhlin, Ohad Shamir, and Karthik Sridharan at NIPS this year goes some ways toward address the first two criticisms. Their paper, “Relax and Randomize: From Value to Algorithms,” (extended version here) is concerned with transforming non-constructive online regret bounds into useful algorithms. Continue reading “Turning Theory into Algorithms”