conftrace_

Tor Lattimore

61 papers · 2013–2025 · 10 conferences · across top CS/AI conferences

Achievements

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+17 more ↓ 🗺️ Taxonomy Completionist (17) 🧭 Keyword Pioneer 🌉 Interdisciplinary Bridge 🌈 Renaissance Researcher (5) 🐣 Hot Topic Early Bird
🐣 Hot Topic Early Bird 🌈 Renaissance Researcher (5) 🌍 Conference Polyglot (10) 🏠 Conference Loyalist (22) 🐺 Lone Wolf (6) 🏆 Grand Slam 🔬 Deep Specialist (26) 🏆 Keyword Champion (3) 🤝 Dynamic Duo (22) 👑 Triple Crown 🗃️ Keyword Collector (187) The Questioner Prolific Year (9) 🚀 Conference Pioneer 📈 Trend Setter 💎 Century Club (61) 🔥 Unstoppable (13)

Conferences

NIPS (22) COLT (13) ICML (11) AISTATS (3) JMLR (3) UAI (3) ALT (2) IJCAI (2) AAAI (1) ICLR (1)

Papers

Thompson Sampling for Bandit Convex Optimisation COLT 2025 Online Newton Method for Bandit Convex Optimisation Extended Abstract COLT 2024 Context-lumpable stochastic bandits NIPS 2023 Probabilistic Inference in Reinforcement Learning Done Right NIPS 2023 Leveraging Demonstrations to Improve Online Learning: Quality Matters ICML 2023 Distributed Contextual Linear Bandits with Minimax Optimal Communication Cost ICML 2023 Linear Partial Monitoring for Sequential Decision Making: Algorithms, Regret Bounds and Applications JMLR 2023 A Second-Order Method for Stochastic Bandit Convex Optimisation COLT 2023 A Lower Bound for Linear and Kernel Regression with Adaptive Covariates COLT 2023 Minimax Regret for Partial Monitoring: Infinite Outcomes and Rustichini’s Regret COLT 2022 Contextual Information-Directed Sampling ICML 2022 Return of the bias: Almost minimax optimal high probability bounds for adversarial linear bandits COLT 2022 Regret Bounds for Information-Directed Reinforcement Learning NIPS 2022 Matrix games with bandit feedback UAI 2021 On the Optimality of Batch Policy Optimization Algorithms ICML 2021 Asymptotically Optimal Information-Directed Sampling COLT 2021 Variational Bayesian Optimistic Sampling NIPS 2021 Information Directed Sampling for Sparse Linear Bandits NIPS 2021 Bandit Phase Retrieval NIPS 2021 Online Sparse Reinforcement Learning AISTATS 2021 Improved Regret for Zeroth-Order Stochastic Convex Bandits COLT 2021 Mirror Descent and the Information Ratio COLT 2021 Sparse Feature Selection Makes Batch Reinforcement Learning More Sample Efficient ICML 2021 Gated Linear Networks AAAI 2021 Learning with Good Feature Representations in Bandits and in RL with a Generative Model ICML 2020 Model Selection in Contextual Stochastic Bandit Problems NIPS 2020 High-Dimensional Sparse Linear Bandits NIPS 2020 Gaussian Gated Linear Networks NIPS 2020 Adaptive Exploration in Linear Contextual Bandit AISTATS 2020 Information Directed Sampling for Linear Partial Monitoring COLT 2020 Exploration by Optimisation in Partial Monitoring COLT 2020 Behaviour Suite for Reinforcement Learning ICLR 2020 Linear bandits with Stochastic Delayed Feedback ICML 2020 Connections Between Mirror Descent, Thompson Sampling and the Information Ratio NIPS 2019 Iterative Budgeted Exponential Search IJCAI 2019 On First-Order Bounds, Variance and Gap-Dependent Bounds for Adversarial Bandits UAI 2019 Cleaning up the neighborhood: A full classification for adversarial partial monitoring ALT 2019 Garbage In, Reward Out: Bootstrapping Exploration in Multi-Armed Bandits ICML 2019 Online Learning to Rank with Features ICML 2019 BubbleRank: Safe Online Learning to Re-Rank via Implicit Click Feedback UAI 2019 An Information-Theoretic Approach to Minimax Regret in Partial Monitoring COLT 2019 A Geometric Perspective on Optimal Representations for Reinforcement Learning NIPS 2019 Single-Agent Policy Tree Search With Guarantees NIPS 2018 TopRank: A practical algorithm for online stochastic ranking NIPS 2018 Refining the Confidence Level for Optimistic Bandit Strategies JMLR 2018 Following the Leader and Fast Rates in Online Linear Prediction: Curved Constraint Sets and Other Regularities JMLR 2017 A Scale Free Algorithm for Stochastic Bandits with Bounded Kurtosis NIPS 2017 The End of Optimism? An Asymptotic Analysis of Finite-Armed Linear Bandits AISTATS 2017 Unifying PAC and Regret: Uniform PAC Bounds for Episodic Reinforcement Learning NIPS 2017 On Thompson Sampling and Asymptotic Optimality IJCAI 2017 Soft-Bayes: Prod for Mixtures of Experts with Log-Loss ALT 2017 Regret Analysis of the Finite-Horizon Gittins Index Strategy for Multi-Armed Bandits COLT 2016 Conservative Bandits ICML 2016 On Explore-Then-Commit strategies NIPS 2016 Causal Bandits: Learning Good Interventions via Causal Inference NIPS 2016 Following the Leader and Fast Rates in Linear Prediction: Curved Constraint Sets and Other Regularities NIPS 2016 Refined Lower Bounds for Adversarial Bandits NIPS 2016 The Pareto Regret Frontier for Bandits NIPS 2015 Linear Multi-Resource Allocation with Semi-Bandit Feedback NIPS 2015 Bounded Regret for Finite-Armed Structured Bandits NIPS 2014 The Sample-Complexity of General Reinforcement Learning ICML 2013