(More) Efficient Reinforcement Learning via Posterior Sampling. - Ian Osband; Daniel Russo 001; Benjamin Van Roy (NIPS, 2013)

Near-optimal Reinforcement Learning in Factored MDPs. - Ian Osband; Benjamin Van Roy (NIPS, 2014)

Model-based Reinforcement Learning and the Eluder Dimension. - Ian Osband; Benjamin Van Roy (NIPS, 2014)

Near-optimal Regret Bounds for Reinforcement Learning in Factored MDPs. - Ian Osband; Benjamin Van Roy (CoRR, 2014)

Bootstrapped Thompson Sampling and Deep Exploration. - Ian Osband; Benjamin Van Roy (CoRR, 2015)

Generalization and Exploration via Randomized Value Functions. - Ian Osband; Benjamin Van Roy; Zheng Wen 002 (ICML, 2016)

Deep Exploration via Bootstrapped DQN. - Ian Osband; Charles Blundell; Alexander Pritzel; Benjamin Van Roy (NIPS, 2016)

Posterior Sampling for Reinforcement Learning Without Episodes. - Ian Osband; Benjamin Van Roy (CoRR, 2016)

On Lower Bounds for Regret in Reinforcement Learning. - Ian Osband; Benjamin Van Roy (CoRR, 2016)

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 (ICML, 2017)

A Tutorial on Thompson Sampling. - Daniel Russo 001; Benjamin Van Roy; Abbas Kazerouni; Ian Osband (CoRR, 2017)

Noisy Networks for Exploration. - 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 (CoRR, 2017)

Learning from Demonstrations for Real World Reinforcement Learning. - 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 (CoRR, 2017)

Gaussian-Dirichlet Posterior Dominance in Sequential Learning. - Ian Osband; Benjamin Van Roy (CoRR, 2017)

On Optimistic versus Randomized Exploration in Reinforcement Learning. - Ian Osband; Benjamin Van Roy (CoRR, 2017)

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; et al. (AAAI, 2018)

Noisy Networks For Exploration. - 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; et al. (ICLR (Poster), 2018)

The Uncertainty Bellman Equation and Exploration. - Brendan O'Donoghue; Ian Osband; Rémi Munos; Volodymyr Mnih (ICML, 2018)

Scalable Coordinated Exploration in Concurrent Reinforcement Learning. - Maria Dimakopoulou; Ian Osband; Benjamin Van Roy (NeurIPS, 2018)

Randomized Prior Functions for Deep Reinforcement Learning. - Ian Osband; John Aslanides; Albin Cassirer (NeurIPS, 2018)

A Tutorial on Thompson Sampling. - Daniel Russo 001; Benjamin Van Roy; Abbas Kazerouni; Ian Osband; Zheng Wen 002 (Found. Trends Mach. Learn., 2018)

Meta-learning of Sequential Strategies. - Pedro A. Ortega; Jane X. Wang; Mark Rowland 001; Tim Genewein; Zeb Kurth-Nelson; Razvan Pascanu; Nicolas Heess; Joel Veness; Alexander Pritzel; Pablo Sprechmann; Siddhant M. Jayakumar; Tom McGrath; et al. (CoRR, 2019)

Deep Exploration via Randomized Value Functions. - Ian Osband; Benjamin Van Roy; Daniel J. Russo; Zheng Wen 002 (J. Mach. Learn. Res., 2019)

Hypermodels for Exploration. - Vikranth Dwaracherla; Xiuyuan Lu; Morteza Ibrahimi; Ian Osband; Zheng Wen 002; Benjamin Van Roy (ICLR, 2020)

Making Sense of Reinforcement Learning and Probabilistic Inference. - Brendan O'Donoghue; Ian Osband; Catalin Ionescu (ICLR, 2020)

Behaviour Suite for Reinforcement Learning. - Ian Osband; Yotam Doron; Matteo Hessel; John Aslanides; Eren Sezener; Andre Saraiva 001; Katrina McKinney; Tor Lattimore; Csaba Szepesvári; Satinder Singh 001; Benjamin Van Roy; Richard S. Sutton; et al. (ICLR, 2020)

Stochastic matrix games with bandit feedback. - Brendan O'Donoghue; Tor Lattimore; Ian Osband (CoRR, 2020)

Matrix games with bandit feedback. - Brendan O'Donoghue; Tor Lattimore; Ian Osband (UAI, 2021)

Epistemic Neural Networks. - Ian Osband; Zheng Wen 002; Mohammad Asghari; Morteza Ibrahimi; Xiyuan Lu; Benjamin Van Roy (CoRR, 2021)

Evaluating Probabilistic Inference in Deep Learning: Beyond Marginal Predictions. - Xiuyuan Lu; Ian Osband; Benjamin Van Roy; Zheng Wen 002 (CoRR, 2021)

Evaluating Predictive Distributions: Does Bayesian Deep Learning Work? - Ian Osband; Zheng Wen 002; Seyed Mohammad Asghari; Vikranth Dwaracherla; Botao Hao; Morteza Ibrahimi; Dieterich Lawson; Xiuyuan Lu; Brendan O'Donoghue; Benjamin Van Roy (CoRR, 2021)

The Neural Testbed: Evaluating Joint Predictions. - Ian Osband; Zheng Wen 002; Seyed Mohammad Asghari; Vikranth Dwaracherla; Xiuyuan Lu; Morteza Ibrahimi; Dieterich Lawson; Botao Hao; Brendan O'Donoghue; Benjamin Van Roy (NeurIPS, 2022)

Evaluating high-order predictive distributions in deep learning. - Ian Osband; Zheng Wen 002; Seyed Mohammad Asghari; Vikranth Dwaracherla; Xiuyuan Lu; Benjamin Van Roy (UAI, 2022)

Robustness of Epinets against Distributional Shifts. - Xiuyuan Lu; Ian Osband; Seyed Mohammad Asghari; Sven Gowal; Vikranth Dwaracherla; Zheng Wen 002; Benjamin Van Roy (CoRR, 2022)

Fine-Tuning Language Models via Epistemic Neural Networks. - Ian Osband; Seyed Mohammad Asghari; Benjamin Van Roy; Nat McAleese; John Aslanides; Geoffrey Irving (CoRR, 2022)

Epistemic Neural Networks. - Ian Osband; Zheng Wen 002; Seyed Mohammad Asghari; Vikranth Dwaracherla; Morteza Ibrahimi; Xiuyuan Lu; Benjamin Van Roy (NeurIPS, 2023)

Approximate Thompson Sampling via Epistemic Neural Networks. - Ian Osband; Zheng Wen 002; Seyed Mohammad Asghari; Vikranth Dwaracherla; Morteza Ibrahimi; Xiuyuan Lu; Benjamin Van Roy (UAI, 2023)

Reinforcement Learning, Bit by Bit. - Xiuyuan Lu; Benjamin Van Roy; Vikranth Dwaracherla; Morteza Ibrahimi; Ian Osband; Zheng Wen 002 (Found. Trends Mach. Learn., 2023)

Ensembles for Uncertainty Estimation: Benefits of Prior Functions and Bootstrapping. - Vikranth Dwaracherla; Zheng Wen 002; Ian Osband; Xiuyuan Lu; Seyed Mohammad Asghari; Benjamin Van Roy (Trans. Mach. Learn. Res., 2023)

OpenAI o1 System Card. - Aaron Jaech; Adam Kalai; Adam Lerer; Adam Richardson; Ahmed El-Kishky; Aiden Low; Alec Helyar; Aleksander Madry; Alex Beutel; Alex Carney; Alex Iftimie; Alex Karpenko; et al. (CoRR, 2024)