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Skip to Search Results- 15Szepesvari, Csaba (Computing Science)
- 4Schuurmans, Dale (Computing Science)
- 1Andras Gyorgy (Imperial College London, U.K.)
- 1Bowling, Michael (Computing Science)
- 1Greiner ,Russell (Computing Science)
- 1Greiner, Russell (Computing Science)
- 2Joulani, Pooria
- 1Abbasi-Yadkori, Yasin
- 1Afkanpour, Arash
- 1Aslan,Ozlem
- 1Balazs, Gabor
- 1Chandak, Kushagra
- 4Online Learning
- 3Learning theory
- 3Reinforcement Learning
- 2Machine Learning
- 2Machine learning
- 1Active learning
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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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Fall 2023
Many real-world tasks in fields such as robotics and control can be formulated as constrained Markov decision processes (CMDPs). In CMDPs, the objective is usually to optimize the return while ensuring some constraints being satisfied at the same time. The primal-dual approach is a common...
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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 2011
This thesis studies the reinforcement learning and planning problems that are modeled by a discounted Markov Decision Process (MDP) with a large state space and finite action space. We follow the value-based approach in which a function approximator is used to estimate the optimal value function....