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Skip to Search Results- 10Online Learning
- 3Machine Learning
- 3Reinforcement Learning
- 2Game Theory
- 2Nash Equilibrium
- 1Abstractions
- 2Joulani, Pooria
- 1Abbasi-Yadkori, Yasin
- 1D'Orazio, Ryan
- 1Elsayed, Mohamed
- 1Jacobsen, Andrew
- 1Morrill, Dustin R
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Fall 2024
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...
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Spring 2016
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...
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Fall 2022
Modern representation learning methods perform well on offline tasks and primarily revolve around batch updates. However, batch updates preclude those methods from focusing on new experience, which is essential for fast online adaptation. In this thesis, we study an online and incremental...
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Learning What to Remember: Strategies for Selective External Memory in Online Reinforcement Learning Agents
DownloadSpring 2019
In realistic environments, intelligent agents must learn to integrate information from their past to inform present decisions. An agent's immediate observations are often limited, and some degree of memory is necessary to complete many everyday tasks. However, an agent cannot remember everything...
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Fall 2012
In this thesis, the multi-armed bandit (MAB) problem in online learning is studied, when the feedback information is not observed immediately but rather after arbitrary, unknown, random delays. In the stochastic" setting when the rewards come from a fixed distribution, an algorithm is given that...
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Spring 2013
In a discrete-time online control problem, a learner makes an effort to control the state of an initially unknown environment so as to minimize the sum of the losses he suffers, where the losses are assumed to depend on the individual state-transitions. Various models of control problems have...
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Fall 2019
We study three problems in the application, design, and analysis of online optimization algorithms for machine learning. First, we consider speeding-up the common task of k-fold cross-validation of online algorithms, and provide TreeCV, an algorithm that reduces the time penalty of k-fold...
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Spring 2013
This work introduces the “online probing” problem: In each round, the learner is able to purchase the values of a subset of features for the current instance. After the learner uses this information to produce a prediction for this instance, it then has the option of paying for seeing the full...
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Fall 2020
Computing a Nash equilibrium in zero-sum games, or more generally saddle point optimization, is a fundamental problem in game theory and machine learning, with applications spanning across a wide variety of domains, from generative modeling and computer vision to super-human AI in imperfect...
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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...