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← Authors

Ian Osband

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

Achievements

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+12 more ↓ 🐣 Hot Topic Early Bird 🌍 Conference Polyglot (5) 🧭 Keyword Pioneer 🌉 Interdisciplinary Bridge 🏃 Academic Marathon (10)
🌈 Renaissance Researcher (5) 🧭 Keyword Pioneer 🐣 Hot Topic Early Bird 🤝 Dynamic Duo (14) 🏆 Keyword Champion (3) 👑 Triple Crown 🗃️ Keyword Collector (67) ❓ The Questioner 🚀 Conference Pioneer 📈 Trend Setter 💎 Century Club (20) 🔥 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) Rémi 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