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Fall 2009

Waugh, Kevin

For zero-sum games, we have efficient solution techniques. Unfortunately, there are interesting games that are too large to solve. Here, a popular approach is to solve an abstract game that models the original game. We assume that more accurate the abstract games result in stronger strategies....

Spring 2010

White, Martha

In this work, we present a unified, general approach to variance reduction in agent evaluation using machine learning to minimize variance. Evaluating an agent's performance in a stochastic setting is necessary for agent development, scientific evaluation, and competitions. Traditionally,...

Fall 2012

Joya, Michael

This thesis provides a description of the cardiac rhythm as a latent chain of heart sound arrivals which occur over time, where each arrival generates a fixed window of observable data that can be described with arbitrary feature functions. This description of the process produces tractable...

Spring 2013

Afkanpour, Arash

Multiple kernel learning (MKL) addresses the problem of learning the kernel function from data. Since a kernel function is associated with an underlying feature space, MKL can be considered as a systematic approach to feature selection. Many of the existing MKL algorithms perform kernel learning...

Fall 2013

Gendron-Bellemare, Marc

This thesis presents new algorithms for dealing with large scale reinforcement learning problems. Central to this work is the Atari 2600 platform, which acts as both a rich evaluation framework and a source of challenges for existing reinforcement learning methods. Three contributions are...

Fall 2013

Vafadost, Mostafa

Given nothing but the generative model of the environment, Monte Carlo Tree Search techniques have recently shown spectacular results on domains previously thought to be intractable. In this thesis we try to develop generic techniques for temporal abstraction inside MCTS that would allow the...

Fall 2013

Cheng, Hao

Due to its wide application in various fields, clustering, as a fundamental unsupervised learning problem, has been intensively investigated over the past few decades. Unfortunately, standard clustering formulations are known to be computationally intractable. Although many convex relaxations of...

Spring 2014

Davidson, Joshua

Efficient, unbiased estimation of agent performance is essential for drawing statistically significant conclusions in multi-agent domains with high outcome variance. Naive Monte Carlo estimation is often insufficient, as it can require a prohibitive number of samples, especially when evaluating...

Spring 2014

Yu, Yaoliang

Many machine learning problems can be formulated under the composite minimization framework which usually involves a smooth loss function and a nonsmooth regularizer. A lot of algorithms have thus been proposed and the main focus has been on first order gradient methods, due to their...

Spring 2015

Rayner, David Christopher Ferguson

Heuristic search is a central problem in artificial intelligence. Among its defining properties is the use of a heuristic, a scalar function mapping pairs of states to an estimate of the actual distance between them. Accurate heuristics are generally correlated with faster query resolution and...