Osband, Ian

24 publications

UAI 2023 Approximate Thompson Sampling via Epistemic Neural Networks Ian Osband, Zheng Wen, Seyed Mohammad Asghari, Vikranth Dwaracherla, Morteza Ibrahimi, Xiuyuan Lu, Benjamin Van Roy

TMLR 2023 Ensembles for Uncertainty Estimation: Benefits of Prior Functions and Bootstrapping Vikranth Dwaracherla, Zheng Wen, Ian Osband, Xiuyuan Lu, Seyed Mohammad Asghari, Benjamin Van Roy

NeurIPS 2023 Epistemic Neural Networks Ian Osband, Zheng Wen, Seyed Mohammad Asghari, Vikranth Dwaracherla, Morteza Ibrahimi, Xiuyuan Lu, Benjamin Van Roy

FnTML 2023 Reinforcement Learning, Bit by Bit Xiuyuan Lu, Benjamin Van Roy, Vikranth Dwaracherla, Morteza Ibrahimi, Ian Osband, Zheng Wen

UAI 2022 Evaluating High-Order Predictive Distributions in Deep Learning Ian Osband, Zheng Wen, Seyed Mohammad Asghari, Vikranth Dwaracherla, Xiuyuan Lu, Benjamin Van Roy

NeurIPS 2022 The Neural Testbed: Evaluating Joint Predictions Ian Osband, Zheng Wen, Seyed Mohammad Asghari, Vikranth Dwaracherla, Xiuyuan Lu, Morteza Ibrahimi, Dieterich Lawson, Botao Hao, Brendan O'Donoghue, Benjamin Van Roy

UAI 2021 Matrix Games with Bandit Feedback Brendan O’Donoghue, Tor Lattimore, Ian Osband

ICLR 2020 Behaviour Suite for Reinforcement Learning Ian Osband, Yotam Doron, Matteo Hessel, John Aslanides, Eren Sezener, Andre Saraiva, Katrina McKinney, Tor Lattimore, Csaba Szepesvari, Satinder Singh, Benjamin Van Roy, Richard Sutton, David Silver, Hado Van Hasselt

ICLR 2020 Hypermodels for Exploration Vikranth Dwaracherla, Xiuyuan Lu, Morteza Ibrahimi, Ian Osband, Zheng Wen, Benjamin Van Roy

ICLR 2020 Making Sense of Reinforcement Learning and Probabilistic Inference Brendan O'Donoghue, Ian Osband, Catalin Ionescu

JMLR 2019 Deep Exploration via Randomized Value Functions Ian Osband, Benjamin Van Roy, Daniel J. Russo, Zheng Wen

FnTML 2018 A Tutorial on Thompson Sampling Daniel Russo, Benjamin Van Roy, Abbas Kazerouni, Ian Osband, Zheng Wen

AAAI 2018 Deep Q-Learning from Demonstrations Todd Hester, Matej Vecerík, Olivier Pietquin, Marc Lanctot, Tom Schaul, Bilal Piot, Dan Horgan, John Quan, Andrew Sendonaris, Ian Osband, Gabriel Dulac-Arnold, John P. Agapiou, Joel Z. Leibo, Audrunas Gruslys

ICLR 2018 Noisy Networks for Exploration Meire Fortunato, Mohammad Gheshlaghi Azar, Bilal Piot, Jacob Menick, Matteo Hessel, Ian Osband, Alex Graves, Volodymyr Mnih, Remi Munos, Demis Hassabis, Olivier Pietquin, Charles Blundell, Shane Legg

NeurIPS 2018 Randomized Prior Functions for Deep Reinforcement Learning Ian Osband, John Aslanides, Albin Cassirer

NeurIPS 2018 Scalable Coordinated Exploration in Concurrent Reinforcement Learning Maria Dimakopoulou, Ian Osband, Benjamin Van Roy

ICML 2018 The Uncertainty Bellman Equation and Exploration Brendan O’Donoghue, Ian Osband, Remi Munos, Vlad Mnih

ICML 2017 Minimax Regret Bounds for Reinforcement Learning Mohammad Gheshlaghi Azar, Ian Osband, Rémi Munos

ICML 2017 Why Is Posterior Sampling Better than Optimism for Reinforcement Learning? Ian Osband, Benjamin Van Roy

NeurIPS 2016 Deep Exploration via Bootstrapped DQN Ian Osband, Charles Blundell, Alexander Pritzel, Benjamin Van Roy

ICML 2016 Generalization and Exploration via Randomized Value Functions Ian Osband, Benjamin Van Roy, Zheng Wen

NeurIPS 2014 Model-Based Reinforcement Learning and the Eluder Dimension Ian Osband, Benjamin Van Roy

NeurIPS 2014 Near-Optimal Reinforcement Learning in Factored MDPs Ian Osband, Benjamin Van Roy

NeurIPS 2013 (More) Efficient Reinforcement Learning via Posterior Sampling Ian Osband, Daniel Russo, Benjamin Van Roy