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Ease-of-Teaching and Language Structure from Emergent Communication.
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Fushan Li and Michael Bowling.
NeurIPS, 2019
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Importance Resampling for Off-policy Prediction.
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Matthew Schlegel, Wesley Chung, Daniel Graves, Jian Qian, Martha White.
NeurIPS, 2019
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Meta-Learning Representations for Continual Learning.
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Khurram Javed and Martha White.
NeurIPS, 2019
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Learning Macroscopic Brain Connectomes via Group-Sparse Factorization.
Farzane Aminmansour, Andrew Patterson, Lei Le, Yisu Peng, Daniel Mitchell, Franco Pestilli, Cesar Caiafa, Russell Greiner, Martha White.
NeurIPS, 2019
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Exponential Family Estimation via Adversarial Dynamics Embedding.
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Bo Dai, Zhen Liu, Hanjun Dai, Niao He, Arthur Gretton, Le Song, Dale Schuurmans.
NeurIPS, 2019
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Maximum Entropy Monte-Carlo Planning.
Chenjun Xiao, Ruitong Huang, Jincheng Mei, Dale Schuurmans,Martin Müller.
NeurIPS, 2019
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Surrogate Objectives for Batch Policy Optimization in One-step Decision Making.
Minmin Chen, Ramki Gummadi, Chris Harris, Dale Schuurmans.
NeurIPS, 2019
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Invertible Convolutional Flow.
Mahdi Karami, Dale Schuurmans, Jascha Sohl-Dickstein, Laurent Dinh, Daniel Duckworth
NeurIPS, 2019
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A Geometric Perspective on Optimal Representations for Reinforcement Learning.
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Marc G. Bellemare, Will Dabney, Robert Dadashi, Adrien Ali Taiga, Pablo Samuel Castro, Nicolas Le Roux, Dale Schuurmans, Tor Lattimore, Clare Lyle
NeurIPS, 2019
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Think out of the "Box": Generically-Constrained Asynchronous Composite Optimization and Hedging.
Pooria Joulani, András György, Csaba Szepesvari.
NeurIPS, 2019
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Detecting Overfitting via Adversarial Examples.
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Roman Werpachowski, András György, Csaba Szepesvari.
NeurIPS, 2019
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Planning with Expectation Models.
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Yi Wan, Muhammad Zaheer, Adam White, Martha White, Richard Sutton.
IJCAI, 2019
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Perturbed-History Exploration in Stochastic Multi-Armed Bandits.
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Branislav Kveton, Csaba Szepesvári, Mohammad Ghavamzadeh, Craig Boutilier.
IJCAI, 2019
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Advantage Amplification in Slowly Evolving Latent-State Environments.
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Martin Mladenov, Ofer Meshi, Jayden Ooi, Dale Schuurmans, Craig Boutilier.
IJCAI, 2019
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On Principled Entropy Exploration in Policy Optimization.
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Jincheng Mei, Chenjun Xiao, Ruitong Huang, Dale Schuurmans, Martin Müller.
IJCAI, 2019
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Hill Climbing on Value Estimates for Search-control in Dyna.
Yangchen Pan, Hengshuai Yao, Amir-massoud Farahmand, Martha White.
IJCAI, 2019
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BubbleRank: Safe Online Learning to Re-Rank via Implicit Click Feedback.
Chang Li, Branislav Kveton, Tor Lattimore, Ilya Markov, Maarten de Rijke, Csaba Szepesvari, Masrour Zoghi
UAI, 2019
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Perturbed-History Exploration in Stochastic Linear Bandits.
Branislav Kveton, Csaba Szepesvari, Mohammad Ghavamzadeh, Craig Boutilier
UAI, 2019
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CapsAndRuns: An Improved Method for Approximately Optimal Algorithm Configuration.
Gellért Weisz, Andras Gyorgy, Csaba Szepesvari.
ICML, 2019
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Garbage In, Reward Out: Bootstrapping Exploration in Multi-Armed Bandits.
Branislav Kveton, Csaba Szepesvari, Sharan Vaswani, Zheng Wen, Tor Lattimore, Mohammad Ghavamzadeh
ICML, 2019
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Online Learning to Rank with Features.
Shuai Li, Tor Lattimore, Csaba Szepesvari
ICML, 2019
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Bayesian Action Decoder for Deep Multi-Agent Reinforcement Learning.
Jakob Foerster, Francis Song, Edward Hughes, Neil Burch, Iain Dunning, Shimon Whiteson, Matthew Botvinick, Michael Bowling
ICML, 2019
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The Value Function Polytope in Reinforcement Learning.
Robert Dadashi, Marc Bellemare, Adrien Ali Taiga, Nicolas Le Roux, Dale Schuurmans
ICML, 2019
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Understanding the Impact of Entropy on Policy Optimization.
Zafarali Ahmed, Nicolas Le Roux, Mohammad Norouzi, Dale Schuurmans
ICML, 2019
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Learning to Generalize from Sparse and Underspecified Rewards.
Rishabh Agarwal, Chen Liang, Dale Schuurmans, Mohammad Norouz
ICML, 2019
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Two-Timescale Networks for Nonlinear Value Function Approximation.
Wesley Chung, Somjit Nath, Ajin Joseph, Martha White.
ICLR, 2019
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Rigorous Agent Evaluation: An Adversarial Approach to Uncover Catastrophic Failures.
Jonathan Uesato, Ananya Kumar, Csaba Szepesvari, Tom Erez, Avraham Ruderma, Keith Anderson, Krishnamurthy Dvijotham, Nicolas Heess, Pushmeet Kohli.
ICLR, 2019
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Prediction in Intelligence: An Empirical Comparison of Off-policy Algorithms on Robots.
Banafsheh Rafiee, Sina Ghiassian, Adam White, Richard Sutton.
AAMAS, 2019
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The Utility of Sparse Representations for Control in Reinforcement Learning.
Vincent Liu, Raksha Kumaraswamy, Lei Le, Martha White.
AAAI, 2019
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An Exponential Tail Bound for the Deleted Estimate.
Abou-Moustafa Karim, Csaba Szepesvari.
AAAI, 2019
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Variance Reduction in Monte Carlo Regret Minimization for Extensive Games using Baselines.
Martin Schmid, Matej Moravcik, Neil Burch, Marc Lanctot, Rudolf Kadlec, Michael Bowling.
AAAI, 2019
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Solving Large Extensive-Form Games with Strategy Constraints.
Trevor Davis, Kevin Waugh, Michael Bowling.
AAAI, 2019
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Meta-descent for Online, Continual Prediction.
Andrew Jacobsen, Matthew Schlegel, Cameron Linke,Thomas Degris, Adam White, Martha White.
AAAI, 2019