← Authors

Ian Osband

20 papers · 2013–2023 · 5 conferences · across top CS/AI conferences

Achievements

πŸŒ‰ Interdisciplinary Bridge 🌍 Conference Polyglot (5) πŸƒ Academic Marathon (10) πŸ—ΊοΈ Taxonomy Completionist (32) 🐝 Cross-Pollinator (14) 🌈 Renaissance Researcher (5) 🧭 Keyword Pioneer 🐣 Hot Topic Early Bird 🀝 Dynamic Duo (14) πŸ† Keyword Champion (3) πŸ‘‘ Triple Crown πŸ—ƒοΈ Keyword Collector (67) ❓ The Questioner πŸš€ Conference Pioneer πŸ“ˆ Trend Setter πŸ’Ž Century Club (20) πŸ”₯ Unstoppable (8)

Conferences

NIPS (8) ICLR (4) ICML (4) UAI (3) JMLR (1)

Top co-authors

Benjamin Van Roy (14) Zheng Wen (7) Xiuyuan Lu (5) Vikranth Dwaracherla (5) Morteza Ibrahimi (4) Seyed Mohammad Asghari (4) RΓ©mi Munos (3) Tor Lattimore (2) Charles Blundell (2) Brendan O'Donoghue (2)

Keywords

regret bound (7) reinforcement learning (6) markov decision process (4) posterior sampling (4) uncertainty estimation (3) randomized value function (3) joint prediction (3) posterior distribution (2) neural network (2) deep reinforcement learning (2) epistemic neural network (2) value iteration (2) predictive distribution (2) ensemble method (2) regret minimization (1) game theory (1) markov decision processes (1) multi-agent reinforcement learning (1) supervised learning (1) exploration (1)

Papers

Approximate Thompson Sampling via Epistemic Neural Networks UAI 2023
Epistemic Neural Networks NIPS 2023
The Neural Testbed: Evaluating Joint Predictions NIPS 2022
Evaluating high-order predictive distributions in deep learning UAI 2022
Matrix games with bandit feedback UAI 2021
Hypermodels for Exploration ICLR 2020
Making Sense of Reinforcement Learning and Probabilistic Inference ICLR 2020
Behaviour Suite for Reinforcement Learning ICLR 2020
Deep Exploration via Randomized Value Functions JMLR 2019
Noisy Networks For Exploration ICLR 2018
The Uncertainty Bellman Equation and Exploration ICML 2018
Randomized Prior Functions for Deep Reinforcement Learning NIPS 2018
Scalable Coordinated Exploration in Concurrent Reinforcement Learning NIPS 2018
Why is Posterior Sampling Better than Optimism for Reinforcement Learning? ICML 2017
Minimax Regret Bounds for Reinforcement Learning ICML 2017
Generalization and Exploration via Randomized Value Functions ICML 2016
Deep Exploration via Bootstrapped DQN NIPS 2016
Model-based Reinforcement Learning and the Eluder Dimension NIPS 2014
Near-optimal Reinforcement Learning in Factored MDPs NIPS 2014
(More) Efficient Reinforcement Learning via Posterior Sampling NIPS 2013