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Skip to Search Results- 29Greiner, Russell (Computing Science)
- 21Bowling, Michael (Computing Science)
- 5Schuurmans, Dale (Computing Science)
- 2Szepesvari, Csaba (Computing Science)
- 2White, Martha (Computing Science)
- 1Bellemare, Marc (Google Brain)
- 2Wen, Junfeng
- 2White, Martha
- 1Afkanpour, Arash
- 1Allen, Felicity R
- 1Bastani, Meysam
- 1Behboudian, Paniz
- 12Machine Learning
- 11Machine learning
- 7Artificial Intelligence
- 7Reinforcement Learning
- 3Game Theory
- 2Abstractions
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Spring 2014
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...
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Fall 2015
Researchers conduct association studies to discover biomarkers in order to gain new biological insight on complex diseases and phenotypes. Although most researchers have intuitions about what defines a biomarker and how to assess the results of an association study, there is neither a formal...
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The challenge of applying machine learning techniques to diagnose schizophrenia using multi-site fMRI data
DownloadSpring 2017
One of the main challenges for the use of machine learning techniques in neuroimaging data is the small n, large p problem. Datasets usually contain only a few hundreds of instances (n), each of which is described using hundreds of thousands of features (p). In this dissertation, we explore the...
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Spring 2018
Patients with Type I Diabetes (T1D) must take insulin injections to prevent the serious long term effects of hyperglycemia â high blood glucose (BG). These patients must also be careful not to inject too much insulin because this could induce hypoglycemia (low BG), which can be fatal. Patients...
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Spring 2018
Decision-making problems with two agents can be modeled as two player games, and a Nash equilibrium is the basic solution concept describing good play in adversarial games. Computing this equilibrium solution for imperfect information games, where players have private, hidden information, is...
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Spring 2020
Reinforcement learning (RL) is a powerful learning paradigm in which agents can learn to maximize sparse and delayed reward signals. Although RL has had many impressive successes in complex domains, learning can take hours, days, or even years of training data. A major challenge of contemporary...
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Spring 2021
This dissertation demonstrates how to utilize data collected previously from different sources to facilitate learning and inference for a target task. Learning from scratch for a target task or environment can be expensive and time-consuming. To address this problem, we make three contributions...
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Spring 2016
Game theoretic solution concepts, such as Nash equilibrium strategies that are optimal against worst case opponents, provide guidance in finding desirable autonomous agent behaviour. In particular, we wish to approximate solutions to complex, dynamic tasks, such as negotiation or bidding in...
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Fall 2015
Extensive-form games are a powerful framework for modeling sequential multi-agent interactions. In extensive-form games with imperfect information, Nash equilibria are generally used as a solution concept, but computing a Nash equilibrium can be intractable in large games. Instead, a variety of...
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Using Survival Prediction Techniques to Learn Consumer-Specific Reservation Price Distributions
DownloadSpring 2015
A consumer's "reservation price" (RP) is the highest price that s/he is willing to pay for one unit of a specified product or service. It is an essential concept in many applications, e.g., personalized pricing, auction and negotiation. While consumers will not volunteer their RPs, we may be able...