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Spring 2023

Johnstonbaugh, Kerrick

Choosing an appropriate action representation is an integral part of solving robotic manipulation problems. Published approaches include latent action models, which train context-conditioned neural networks to map lowdimensional latent actions to high-dimensional actuation commands. Such models...

Spring 2021

Linke, Cameron

Learning about many things can provide numerous benefits to a reinforcement learning system. For example, learning many auxiliary value functions, in addition to optimizing the environmental reward, appears to improve both exploration and representation learning. The question we tackle in this...

Fall 2024

Jacobsen, Andrew

Over the last decade, machine learning (ML) has lead to advances in many fields, such as computer vision, online decision-making, robotics, natural language processing, and many others. The algorithms driving these successes typically have one or more user-specified free variables called...

2005

Lu, Xiaohu

Spring 2010

Shi, Jichuan

This thesis is concerned with a novel local threshold segmentation algorithm for digital images incorporating shape information. In image segmentation, most local threshold algorithms are based only on intensity analysis. In many applications where an image contains objects with a similar shape,...

2000

Polak, Mark John

Spring 2016

Neufeld, James, P

Monte Carlo methods are a simple, effective, and widely deployed way of approximating integrals that prove too challenging for deterministic approaches. This thesis presents a number of contributions to the field of adaptive Monte Carlo methods. That is, approaches that automatically adjust the...

Spring 2023

Anvari, Hamidreza

We introduce "optimization through protocol selection" (OPS) as a technique to improve bulk-data transfer on shared wide-area networks (WANs). Instead of tuning the parameters of a network protocol, our empirical results show that the selection of the protocol itself can result in up to four...

1974

Wesley, Brian J.

Spring 2015

Das Gupta, Ujjwal

Much of the focus on finding good representations in reinforcement learning has been on learning complex non-linear predictors of value. Methods like policy gradient, that do not learn a value function and instead directly represent policy, often need fewer parameters to learn good policies....