Ian Osband

Publication Activity (10 Years)

Years Active: 2013-2023
Publications (10 Years): 49

Top Topics

Probabilistic Inference

Metropolis Hastings

Reinforcement Learning

Top Venues

Publications

Vikranth Dwaracherla, Zheng Wen, Ian Osband, Xiuyuan Lu, Seyed Mohammad Asghari, Benjamin Van Roy
Ensembles for Uncertainty Estimation: Benefits of Prior Functions and Bootstrapping. Trans. Mach. Learn. Res. 2023 (2023)
Ian Osband, Zheng Wen, Seyed Mohammad Asghari, Vikranth Dwaracherla, Morteza Ibrahimi, Xiuyuan Lu, Benjamin Van Roy
Approximate Thompson Sampling via Epistemic Neural Networks. UAI (2023)
Ian Osband, Zheng Wen, Seyed Mohammad Asghari, Vikranth Dwaracherla, Morteza Ibrahimi, Xiuyuan Lu, Benjamin Van Roy
Approximate Thompson Sampling via Epistemic Neural Networks. CoRR (2023)
Ian Osband, Zheng Wen, Seyed Mohammad Asghari, Vikranth Dwaracherla, Morteza Ibrahimi, Xiuyuan Lu, Benjamin Van Roy
Epistemic Neural Networks. NeurIPS (2023)
Xiuyuan Lu, Benjamin Van Roy, Vikranth Dwaracherla, Morteza Ibrahimi, Ian Osband, Zheng Wen
Reinforcement Learning, Bit by Bit. Found. Trends Mach. Learn. 16 (6) (2023)
Xiuyuan Lu, Ian Osband, Seyed Mohammad Asghari, Sven Gowal, Vikranth Dwaracherla, Zheng Wen, Benjamin Van Roy
Robustness of Epinets against Distributional Shifts. CoRR (2022)
Ian Osband, Zheng Wen, Seyed Mohammad Asghari, Vikranth Dwaracherla, Xiuyuan Lu, Benjamin Van Roy
Evaluating high-order predictive distributions in deep learning. UAI (2022)
Ian Osband, Seyed Mohammad Asghari, Benjamin Van Roy, Nat McAleese, John Aslanides, Geoffrey Irving
Fine-Tuning Language Models via Epistemic Neural Networks. CoRR (2022)
Vikranth Dwaracherla, Zheng Wen, Ian Osband, Xiuyuan Lu, Seyed Mohammad Asghari, Benjamin Van Roy
Ensembles for Uncertainty Estimation: Benefits of Prior Functions and Bootstrapping. CoRR (2022)
Ian Osband, Zheng Wen, Seyed Mohammad Asghari, Vikranth Dwaracherla, Xiuyuan Lu, Benjamin Van Roy
Evaluating High-Order Predictive Distributions in Deep Learning. CoRR (2022)
Ian Osband, Zheng Wen, Seyed Mohammad Asghari, Vikranth Dwaracherla, Xiuyuan Lu, Morteza Ibrahimi, Dieterich Lawson, Botao Hao, Brendan O'Donoghue, Benjamin Van Roy
The Neural Testbed: Evaluating Joint Predictions. NeurIPS (2022)
Ian Osband, Zheng Wen, Seyed Mohammad Asghari, Vikranth Dwaracherla, Botao Hao, Morteza Ibrahimi, Dieterich Lawson, Xiuyuan Lu, Brendan O'Donoghue, Benjamin Van Roy
Evaluating Predictive Distributions: Does Bayesian Deep Learning Work? CoRR (2021)
Brendan O'Donoghue, Tor Lattimore, Ian Osband
Matrix games with bandit feedback. UAI (2021)
Xiuyuan Lu, Ian Osband, Benjamin Van Roy, Zheng Wen
Evaluating Probabilistic Inference in Deep Learning: Beyond Marginal Predictions. CoRR (2021)
Xiuyuan Lu, Benjamin Van Roy, Vikranth Dwaracherla, Morteza Ibrahimi, Ian Osband, Zheng Wen
Reinforcement Learning, Bit by Bit. CoRR (2021)
Ian Osband, Zheng Wen, Mohammad Asghari, Morteza Ibrahimi, Xiyuan Lu, Benjamin Van Roy
Epistemic Neural Networks. CoRR (2021)
Ian Osband, Yotam Doron, Matteo Hessel, John Aslanides, Eren Sezener, Andre Saraiva, Katrina McKinney, Tor Lattimore, Csaba Szepesvári, Satinder Singh, Benjamin Van Roy, Richard S. Sutton, David Silver, Hado van Hasselt
Behaviour Suite for Reinforcement Learning. ICLR (2020)
Brendan O'Donoghue, Tor Lattimore, Ian Osband
Stochastic matrix games with bandit feedback. CoRR (2020)
Brendan O'Donoghue, Ian Osband, Catalin Ionescu
Making Sense of Reinforcement Learning and Probabilistic Inference. ICLR (2020)
Vikranth Dwaracherla, Xiuyuan Lu, Morteza Ibrahimi, Ian Osband, Zheng Wen, Benjamin Van Roy
Hypermodels for Exploration. ICLR (2020)
Brendan O'Donoghue, Ian Osband, Catalin Ionescu
Making Sense of Reinforcement Learning and Probabilistic Inference. CoRR (2020)
Vikranth Dwaracherla, Xiuyuan Lu, Morteza Ibrahimi, Ian Osband, Zheng Wen, Benjamin Van Roy
Hypermodels for Exploration. CoRR (2020)
Ian Osband, Yotam Doron, Matteo Hessel, John Aslanides, Eren Sezener, Andre Saraiva, Katrina McKinney, Tor Lattimore, Csaba Szepesvári, Satinder Singh, Benjamin Van Roy, Richard S. Sutton, David Silver, Hado van Hasselt
Behaviour Suite for Reinforcement Learning. CoRR (2019)
Pedro A. Ortega, Jane X. Wang, Mark Rowland, Tim Genewein, Zeb Kurth-Nelson, Razvan Pascanu, Nicolas Heess, Joel Veness, Alexander Pritzel, Pablo Sprechmann, Siddhant M. Jayakumar, Tom McGrath, Kevin J. Miller, Mohammad Gheshlaghi Azar, Ian Osband, Neil C. Rabinowitz, András György, Silvia Chiappa, Simon Osindero, Yee Whye Teh, Hado van Hasselt, Nando de Freitas, Matthew Botvinick, Shane Legg
Meta-learning of Sequential Strategies. CoRR (2019)
Ian Osband, Benjamin Van Roy, Daniel J. Russo, Zheng Wen
Deep Exploration via Randomized Value Functions. J. Mach. Learn. Res. 20 (2019)
Maria Dimakopoulou, Ian Osband, Benjamin Van Roy
Scalable Coordinated Exploration in Concurrent Reinforcement Learning. NeurIPS (2018)
Ian Osband, John Aslanides, Albin Cassirer
Randomized Prior Functions for Deep Reinforcement Learning. NeurIPS (2018)
Maria Dimakopoulou, Ian Osband, Benjamin Van Roy
Scalable Coordinated Exploration in Concurrent Reinforcement Learning. CoRR (2018)
Brendan O'Donoghue, Ian Osband, Rémi Munos, Volodymyr Mnih
The Uncertainty Bellman Equation and Exploration. ICML (2018)
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
Deep Q-learning From Demonstrations. AAAI (2018)
Ian Osband, John Aslanides, Albin Cassirer
Randomized Prior Functions for Deep Reinforcement Learning. CoRR (2018)
Daniel Russo, Benjamin Van Roy, Abbas Kazerouni, Ian Osband, Zheng Wen
A Tutorial on Thompson Sampling. Found. Trends Mach. Learn. 11 (1) (2018)
Meire Fortunato, Mohammad Gheshlaghi Azar, Bilal Piot, Jacob Menick, Matteo Hessel, Ian Osband, Alex Graves, Volodymyr Mnih, Rémi Munos, Demis Hassabis, Olivier Pietquin, Charles Blundell, Shane Legg
Noisy Networks For Exploration. ICLR (Poster) (2018)
Ian Osband, Daniel Russo, Zheng Wen, Benjamin Van Roy
Deep Exploration via Randomized Value Functions. CoRR (2017)
Ian Osband, Benjamin Van Roy
On Optimistic versus Randomized Exploration in Reinforcement Learning. CoRR (2017)
Mohammad Gheshlaghi Azar, Ian Osband, Rémi Munos
Minimax Regret Bounds for Reinforcement Learning. CoRR (2017)
Mohammad Gheshlaghi Azar, Ian Osband, Rémi Munos
Minimax Regret Bounds for Reinforcement Learning. ICML (2017)
Todd Hester, Matej Vecerík, Olivier Pietquin, Marc Lanctot, Tom Schaul, Bilal Piot, Andrew Sendonaris, Gabriel Dulac-Arnold, Ian Osband, John P. Agapiou, Joel Z. Leibo, Audrunas Gruslys
Learning from Demonstrations for Real World Reinforcement Learning. CoRR (2017)
Ian Osband, Benjamin Van Roy
Gaussian-Dirichlet Posterior Dominance in Sequential Learning. CoRR (2017)
Brendan O'Donoghue, Ian Osband, Rémi Munos, Volodymyr Mnih
The Uncertainty Bellman Equation and Exploration. CoRR (2017)
Meire Fortunato, Mohammad Gheshlaghi Azar, Bilal Piot, Jacob Menick, Ian Osband, Alex Graves, Vlad Mnih, Rémi Munos, Demis Hassabis, Olivier Pietquin, Charles Blundell, Shane Legg
Noisy Networks for Exploration. CoRR (2017)
Daniel Russo, Benjamin Van Roy, Abbas Kazerouni, Ian Osband
A Tutorial on Thompson Sampling. CoRR (2017)
Ian Osband, Benjamin Van Roy
Why is Posterior Sampling Better than Optimism for Reinforcement Learning? ICML (2017)
Ian Osband, Benjamin Van Roy, Zheng Wen
Generalization and Exploration via Randomized Value Functions. ICML (2016)
Ian Osband, Benjamin Van Roy
Posterior Sampling for Reinforcement Learning Without Episodes. CoRR (2016)
Ian Osband, Charles Blundell, Alexander Pritzel, Benjamin Van Roy
Deep Exploration via Bootstrapped DQN. NIPS (2016)
Ian Osband, Charles Blundell, Alexander Pritzel, Benjamin Van Roy
Deep Exploration via Bootstrapped DQN. CoRR (2016)
Ian Osband, Benjamin Van Roy
Why is Posterior Sampling Better than Optimism for Reinforcement Learning. CoRR (2016)
Ian Osband, Benjamin Van Roy
On Lower Bounds for Regret in Reinforcement Learning. CoRR (2016)
Ian Osband, Benjamin Van Roy
Bootstrapped Thompson Sampling and Deep Exploration. CoRR (2015)
Ian Osband, Benjamin Van Roy
Near-optimal Reinforcement Learning in Factored MDPs. NIPS (2014)
Ian Osband, Benjamin Van Roy
Model-based Reinforcement Learning and the Eluder Dimension. CoRR (2014)
Ian Osband, Benjamin Van Roy
Model-based Reinforcement Learning and the Eluder Dimension. NIPS (2014)
Ian Osband, Benjamin Van Roy
Near-optimal Regret Bounds for Reinforcement Learning in Factored MDPs. CoRR (2014)
Ian Osband, Daniel Russo, Benjamin Van Roy
(More) Efficient Reinforcement Learning via Posterior Sampling. CoRR (2013)
Ian Osband, Daniel Russo, Benjamin Van Roy
(More) Efficient Reinforcement Learning via Posterior Sampling. NIPS (2013)