Tor Lattimore
61 papers · 2013–2025 · 10 conferences · across top CS/AI conferences
Achievements
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🗺️ Taxonomy Completionist (17) 🧭 Keyword Pioneer 🌉 Interdisciplinary Bridge 🌈 Renaissance Researcher (5) 🐣 Hot Topic Early Bird
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(22)
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(6)
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(26)
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Conferences
NIPS (22)
COLT (13)
ICML (11)
AISTATS (3)
JMLR (3)
UAI (3)
ALT (2)
IJCAI (2)
AAAI (1)
ICLR (1)
Top co-authors
Keywords
regret bound
(35)
online learning
(18)
multi-armed bandit
(18)
contextual bandit
(8)
linear bandit
(7)
thompson sampling
(7)
partial monitoring
(6)
adversarial bandit
(6)
reinforcement learning
(5)
minimax regret
(5)
stochastic bandit
(5)
bayesian regret
(5)
stochastic optimization
(5)
regret minimization
(4)
online algorithm
(4)
adversarial setting
(3)
zeroth-order optimization
(3)
learning to rank
(3)
bayesian inference
(3)
sample complexity
(3)
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