IO
Ian Osband
cs.LGstat.MLcs.AImath.OCcs.CLcs.SYeess.SYcs.CVcs.CYcs.SD
On Valency
published · living versionsW_h6wnc74b·v1 · currentpublished
W_ens2g6jf·v1published
W_zs59fjhv·v1published
W_caxh5thz·v1published
Preprints & journals
41 papers in the corpus · 2013–2026Planning to Learn2610.03667v1 · Ian Osband2026 · 0 citationsarXiv
Delightful Exploration2605.13287v1 · Ian Osband2026 · 0 citationsarXiv
Delightful Gradients Accelerate Corner Escape2605.11908v1 · Jincheng Mei, Ian Osband2026 · 0 citationsarXiv
Delightful Distributed Policy Gradient2603.20521v2 · Ian Osband2026 · 0 citationsarXiv
Does This Gradient Spark Joy?2603.20526v1 · Ian Osband2026 · 0 citationsarXiv
Delightful Policy Gradient2603.14608v1 · Ian Osband2026 · 0 citationsarXiv
OpenAI o1 System Card2412.16720v2 · OpenAI: Aaron Jaech, Adam Kalai, Adam Lerer et al.2024 · 50 citationsarXiv
GPT-4o System Card2410.21276v1 · OpenAI: Aaron Hurst, Adam Lerer, Adam P. Goucher et al.2024 · 179 citationsarXiv
Approximate Thompson Sampling via Epistemic Neural Networks2302.09205v1 · Ian Osband, Zheng Wen, Seyed Mohammad Asghari et al.2023 · 1 citationarXiv
Fine-Tuning Language Models via Epistemic Neural Networks2211.01568v2 · Ian Osband, Seyed Mohammad Asghari, Benjamin Van Roy et al.2022 · 3 citationsarXiv
Robustness of Epinets against Distributional Shifts2207.00137v1 · Xiuyuan Lu, Ian Osband, Seyed Mohammad Asghari et al.2022 · 1 citationarXiv
Ensembles for Uncertainty Estimation: Benefits of Prior Functions and Bootstrapping2206.03633v1 · Vikranth Dwaracherla, Zheng Wen, Ian Osband et al.2022 · 6 citationsarXiv
Evaluating High-Order Predictive Distributions in Deep Learning2202.13509v1 · Ian Osband, Zheng Wen, Seyed Mohammad Asghari et al.2022 · 1 citationarXiv
The Neural Testbed: Evaluating Joint Predictions2110.04629v4 · Ian Osband, Zheng Wen, Seyed Mohammad Asghari et al.2021 · 2 citationsarXiv
From Predictions to Decisions: The Importance of Joint Predictive Distributions2107.09224v3 · Zheng Wen, Ian Osband, Chao Qin et al.2021 · 0 citationsarXiv
Epistemic Neural Networks2107.08924v8 · Ian Osband, Zheng Wen, Seyed Mohammad Asghari et al.2021 · 34 citationsarXiv
Reinforcement Learning, Bit by Bit2103.04047v8 · Xiuyuan Lu, Benjamin Van Roy, Vikranth Dwaracherla et al.2021 · 24 citationsarXiv
Hypermodels for Exploration2006.07464v1 · Vikranth Dwaracherla, Xiuyuan Lu, Morteza Ibrahimi et al.2020 · 19 citationsarXiv
Matrix games with bandit feedback2006.05145v2 · Brendan O'Donoghue, Tor Lattimore, Ian Osband2020 · 1 citationarXiv
Making Sense of Reinforcement Learning and Probabilistic Inference2001.00805v3 · Brendan O'Donoghue, Ian Osband, Catalin Ionescu2020 · 16 citationsarXiv
Behaviour Suite for Reinforcement Learning1908.03568v3 · Ian Osband, Yotam Doron, Matteo Hessel et al.2019 · 58 citationsarXiv
Meta-learning of Sequential Strategies1905.03030v2 · Pedro A. Ortega, Jane X. Wang, Mark Rowland et al.2019 · 47 citationsarXiv
Randomized Prior Functions for Deep Reinforcement Learning1806.03335v2 · Ian Osband, John Aslanides, Albin Cassirer2018 · 193 citationsarXiv
Scalable Coordinated Exploration in Concurrent Reinforcement Learning1805.08948v2 · Maria Dimakopoulou, Ian Osband, Benjamin Van Roy2018 · 16 citationsarXiv
The Uncertainty Bellman Equation and Exploration1709.05380v4 · Brendan O'Donoghue, Ian Osband, Remi Munos et al.2017 · 108 citationsarXiv
A Tutorial on Thompson Sampling1707.02038v3 · Daniel Russo, Benjamin Van Roy, Abbas Kazerouni et al.2017 · 842 citationsFoundations and Trends in Machine Learning, Vol. 11, No. 1, pp. 1-96, 2018
Noisy Networks for Exploration1706.10295v3 · Meire Fortunato, Mohammad Gheshlaghi Azar, Bilal Piot et al.2017 · 665 citationsarXiv
On Optimistic versus Randomized Exploration in Reinforcement Learning1706.04241v1 · Ian Osband, Benjamin Van Roy2017 · 8 citationsarXiv
Deep Q-learning from Demonstrations1704.03732v4 · Todd Hester, Matej Vecerik, Olivier Pietquin et al.2017 · 1,021 citationsarXiv
Deep Exploration via Randomized Value Functions1703.07608v5 · Ian Osband, Benjamin Van Roy, Daniel Russo et al.2017 · 153 citationsarXiv
Minimax Regret Bounds for Reinforcement Learning1703.05449v2 · Mohammad Gheshlaghi Azar, Ian Osband, R'emi Munos2017 · 124 citationsarXiv
Gaussian-Dirichlet Posterior Dominance in Sequential Learning1702.04126v3 · Ian Osband, Benjamin Van Roy2017 · 1 citationarXiv
Posterior Sampling for Reinforcement Learning Without Episodes1608.02731v1 · Ian Osband, Benjamin Van Roy2016 · 7 citationsarXiv
On Lower Bounds for Regret in Reinforcement Learning1608.02732v1 · Ian Osband, Benjamin Van Roy2016 · 46 citationsarXivon Valency
Why is Posterior Sampling Better than Optimism for Reinforcement Learning?1607.00215v3 · Ian Osband, Benjamin Van Roy2016 · 125 citationsarXivon Valency
Deep Exploration via Bootstrapped DQN1602.04621v3 · Ian Osband, Charles Blundell, Alexander Pritzel et al.2016 · 838 citationsarXiv
Bootstrapped Thompson Sampling and Deep Exploration1507.00300v1 · Ian Osband, Benjamin Van Roy2015 · 53 citationsarXivon Valency
Model-based Reinforcement Learning and the Eluder Dimension1406.1853v2 · Ian Osband, Benjamin Van Roy2014 · 75 citationsarXiv
Near-optimal Reinforcement Learning in Factored MDPs1403.3741v3 · Ian Osband, Benjamin Van Roy2014 · 48 citationsarXivon Valency
Generalization and Exploration via Randomized Value Functions1402.0635v3 · Ian Osband, Benjamin Van Roy, Zheng Wen2014 · 169 citationsarXiv
(More) Efficient Reinforcement Learning via Posterior Sampling1306.0940v5 · Ian Osband, Daniel Russo, Benjamin Van Roy2013 · 237 citationsarXivon Valency
Career total: 57 works. 41 are in this corpus.
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