Yoav Freund
CS 789 THEORY SEMINAR [home]
Speaker: Yoav Freund
Affiliation: Banter, Inc.
Date: Monday, March 31, 2003
Title: A Black Box Approach to Machine Learning
Abstract:
I will present a new approach to the
study and development of machine learning algorithms.
The goal of a learning algorithm is to produce a good decision algorithm for a
repetitive decision task. A decision algorithm receives as input an instance
(sensory data) and outputs a decision
(an action). After the decision has been made, there is a measurable outcome. We
assume the existence of a loss function which quantifies the utility of a
decision with respect to an outcome.
My main focus is on binary classification tasks. In this case, the decision is
binary, the outcome is binary, and the loss is 1 if the decision and outcome
don't match and 0 if they do.
Given these definitions and a source of instances and outcomes, we can evaluate
the performance of any decision algorithm. In this way we can ignore the
internal workings of the decision algorithm and treat it as a "black
box".
The practical advantage of the black-box approach is that it gives us a
measuring stick for comparing all types of decision algorithms, regardless of
how they are constructed or analyzed. By extension, it gives us a way of
comparing all types of learning algorithms.
From a theoretical point of view, the task is to develop and analyze learning
algorithms that combine several black boxes into a new box with better
performance. The resulting box can then be used as a component in other
combining algorithms.
The theoretical guarantees that we prove are relative rather than absolute.
Absolute guarantees, which are the norm in theoretical analysis of adaptive
algorithms, make detailed assumptions about the data distribution. Instead, we
prove bounds which relate the performance of the learning algorithm to the
performance of the black-box algorithms it combines. These learning algorithms
include boosting, online combining of expert advice, and pseudo-Bayesian
averaging.