Ian Osband

发表

Risk versus Uncertainty in Deep Learning: Bayes, Bootstrap and the Dangers of Dropout

Ian Osband, Ian Osband, 2016 .

Noisy Networks for Exploration pdf

Shane Legg, Alex Graves, Demis Hassabis, 2017, ICLR.

Bootstrapped Thompson Sampling and Deep Exploration pdf

Benjamin Van Roy, Ian Osband, Ian Osband, 2015, ArXiv.

Gaussian-Dirichlet Posterior Dominance in Sequential Learning pdf

Benjamin Van Roy, Ian Osband, Ian Osband, 2017, ArXiv.

Deep Q-learning From Demonstrations

Tom Schaul, Andrew Sendonaris, Joel Z. Leibo, 2017, AAAI.

Near-optimal Reinforcement Learning in Factored MDPs

Benjamin Van Roy, Ian Osband, Ian Osband, 2014, NIPS.

Making Sense of Reinforcement Learning and Probabilistic Inference pdf

Ian Osband, Brendan O'Donoghue, Catalin Ionescu, 2020, ICLR.

Stochastic matrix games with bandit feedback pdf

Tor Lattimore, Ian Osband, Brendan O'Donoghue, 2020, ArXiv.

Meta-learning of Sequential Strategies pdf

Yee Whye Teh, Razvan Pascanu, Nando de Freitas, 2019, ArXiv.

Deep Learning for Time Series Modeling CS 229 Final Project Report

Ian Osband, Enzo Busseti, Scott Wong, 2012 .

On Optimistic versus Randomized Exploration in Reinforcement Learning pdf

Benjamin Van Roy, Ian Osband, 2017, ArXiv.

Near-optimal Regret Bounds for Reinforcement Learning in Factored MDPs pdf

Benjamin Van Roy, Ian Osband, Ian Osband, 2014, ArXiv.

Posterior Sampling for Reinforcement Learning Without Episodes pdf

Benjamin Van Roy, Ian Osband, 2016, ArXiv.

Minimax Regret Bounds for Reinforcement Learning

Rémi Munos, Ian Osband, Mohammad Gheshlaghi Azar, 2017, ICML.

The Uncertainty Bellman Equation and Exploration

Ian Osband, Brendan O'Donoghue, Volodymyr Mnih, 2017, ICML.

Why is Posterior Sampling Better than Optimism for Reinforcement Learning? pdf

Benjamin Van Roy, Ian Osband, Ian Osband, 2016, ICML.

Generalization and Exploration via Randomized Value Functions pdf

Benjamin Van Roy, Ian Osband, Zheng Wen, 2014, ICML.

Behaviour Suite for Reinforcement Learning pdf

Tor Lattimore, Richard S. Sutton, Csaba Szepesvári, 2019, ICLR.

On Lower Bounds for Regret in Reinforcement Learning pdf

Benjamin Van Roy, Ian Osband, Ian Osband, 2016, ArXiv.

Deep Exploration via Bootstrapped DQN

Benjamin Van Roy, Charles Blundell, Ian Osband, 2016, NIPS.

(More) Efficient Reinforcement Learning via Posterior Sampling

Benjamin Van Roy, Ian Osband, Daniel Russo, 2013, NIPS.

Model-based Reinforcement Learning and the Eluder Dimension

Benjamin Van Roy, Ian Osband, Ian Osband, 2014, NIPS.

Learning from Demonstrations for Real World Reinforcement Learning pdf

Tom Schaul, Andrew Sendonaris, Joel Z. Leibo, 2017, ArXiv.

Scalable Coordinated Exploration in Concurrent Reinforcement Learning

Benjamin Van Roy, Ian Osband, Maria Dimakopoulou, 2018, NeurIPS.

A Tutorial on Thompson Sampling

Benjamin Van Roy, Ian Osband, Daniel Russo, 2017, Found. Trends Mach. Learn..

Hypermodels for Exploration pdf

Benjamin Van Roy, Ian Osband, Xiuyuan Lu, 2020, ICLR.

Randomized Prior Functions for Deep Reinforcement Learning

Albin Cassirer, Ian Osband, John Aslanides, 2018, NeurIPS.

Deep Exploration via Randomized Value Functions

Zheng Wen, Benjamin Van Roy, Ian Osband, 2017, J. Mach. Learn. Res..

Epistemic Neural Networks pdf

Zheng Wen, Benjamin Van Roy, Ian Osband, 2021, ArXiv.

Evaluating Probabilistic Inference in Deep Learning: Beyond Marginal Predictions pdf

Zheng Wen, Benjamin Van Roy, Ian Osband, 2021, ArXiv.

Matrix games with bandit feedback

Tor Lattimore, Ian Osband, Brendan O'Donoghue, 2020, UAI.

Evaluating Predictive Distributions: Does Bayesian Deep Learning Work? pdf

Seyed Mohammad Asghari, Benjamin Van Roy, Ian Osband, 2021, ArXiv.

Reinforcement Learning, Bit by Bit pdf

Zheng Wen, Benjamin Van Roy, Ian Osband, 2021, Found. Trends Mach. Learn..