Tor Lattimore

Most-cited papers

Bandit Algorithms
Tor Lattimore, Csaba Szepesvari
20203,526 citations Open access
Semantic Scholar
Unifying PAC and Regret: Uniform PAC Bounds for Episodic Reinforcement Learning
Christoph Dann, Tor Lattimore, Emma Brunskill
2017Neural Information Processing Systems336 citations
Semantic Scholar
Degenerate Feedback Loops in Recommender Systems
Ray Jiang, S. Chiappa, Tor Lattimore, A. György +1 more
2019AAAI/ACM Conference on AI, Ethics, and Society265 citations Open access
Semantic Scholar
Behaviour Suite for Reinforcement Learning
Ian Osband, Yotam Doron, Matteo Hessel, John Aslanides +10 more
2019International Conference on Learning Representations222 citations
Semantic Scholar
Causal Bandits: Learning Good Interventions via Causal Inference
Finnian Lattimore, Tor Lattimore, Mark D. Reid
2016Neural Information Processing Systems207 citations
Semantic Scholar
Learning with Good Feature Representations in Bandits and in RL with a Generative Model
G. Weisz, Tor Lattimore, Csaba Szepesvari
2019International Conference on Machine Learning200 citations
Semantic Scholar
On Explore-Then-Commit strategies
Aurélien Garivier, E. Kaufmann, Tor Lattimore
2016Neural Information Processing Systems137 citations
Semantic Scholar
Conservative Bandits
Yifan Wu, Roshan Shariff, Tor Lattimore, Csaba Szepesvari
2016International Conference on Machine Learning123 citations
Semantic Scholar
The End of Optimism? An Asymptotic Analysis of Finite-Armed Linear Bandits
Tor Lattimore, Csaba Szepesvari
2016International Conference on Artificial Intelligence and Statistics117 citations
Semantic Scholar
Model Selection in Contextual Stochastic Bandit Problems
Aldo Pacchiano, My Phan, Yasin Abbasi-Yadkori, Anup B. Rao +3 more
2020Neural Information Processing Systems108 citations
Semantic Scholar
A Geometric Perspective on Optimal Representations for Reinforcement Learning
Marc G. Bellemare, Will Dabney, Robert Dadashi, Adrien Ali Taïga +5 more
2019Neural Information Processing Systems99 citations
Semantic Scholar
Following the Leader and Fast Rates in Online Linear Prediction: Curved Constraint Sets and Other Regularities
Ruitong Huang, Tor Lattimore, A. György, Csaba Szepesvari
2017Journal of machine learning research84 citations
Semantic Scholar
Adaptive Exploration in Linear Contextual Bandit
Botao Hao, Tor Lattimore, Csaba Szepesvari
2019International Conference on Artificial Intelligence and Statistics83 citations
Semantic Scholar
Linear bandits with Stochastic Delayed Feedback
Claire Vernade, A. Carpentier, Tor Lattimore, Giovanni Zappella +2 more
2018International Conference on Machine Learning83 citations
Semantic Scholar
Garbage In, Reward Out: Bootstrapping Exploration in Multi-Armed Bandits
Branislav Kveton, Csaba Szepesvari, Sharan Vaswani, Zheng Wen +2 more
2018International Conference on Machine Learning79 citations
Semantic Scholar
High-Dimensional Sparse Linear Bandits
Botao Hao, Tor Lattimore, Mengdi Wang
2020Neural Information Processing Systems77 citations
Semantic Scholar
TopRank: A practical algorithm for online stochastic ranking
Tor Lattimore, Branislav Kveton, Shuai Li, Csaba Szepesvari
2018Neural Information Processing Systems77 citations
Semantic Scholar
An Information-Theoretic Approach to Minimax Regret in Partial Monitoring
Tor Lattimore, Csaba Szepesvari
2019Annual Conference Computational Learning Theory73 citations
Semantic Scholar
Refined Lower Bounds for Adversarial Bandits
Sébastien Gerchinovitz, Tor Lattimore
2016Neural Information Processing Systems65 citations
Semantic Scholar
Linear Multi-Resource Allocation with Semi-Bandit Feedback
Tor Lattimore, K. Crammer, Csaba Szepesvari
2015Neural Information Processing Systems59 citations
Semantic Scholar
Refining the Confidence Level for Optimistic Bandit Strategies
Tor Lattimore
2018Journal of machine learning research57 citations
Semantic Scholar
Information Directed Sampling for Linear Partial Monitoring
Johannes Kirschner, Tor Lattimore, A. Krause
2020Annual Conference Computational Learning Theory56 citations
Semantic Scholar
Gated Linear Networks
J. Veness, Tor Lattimore, D. Budden, Avishkar Bhoopchand +7 more
2019AAAI Conference on Artificial Intelligence55 citations Open access
Semantic Scholar
On Thompson Sampling and Asymptotic Optimality
Jan Leike, Tor Lattimore, Laurent Orseau, Marcus Hutter
2017International Joint Conference on Artificial Intelligence54 citations Open access
Semantic Scholar
Improved Regret for Zeroth-Order Adversarial Bandit Convex Optimisation
Tor Lattimore
2020arXiv.org53 citations Open access
Semantic Scholar
Regret Analysis of the Finite-Horizon Gittins Index Strategy for Multi-Armed Bandits
Tor Lattimore
2015Annual Conference Computational Learning Theory51 citations
Semantic Scholar
Single-Agent Policy Tree Search With Guarantees
Laurent Orseau, Levi H. S. Lelis, Tor Lattimore, T. Weber
2018Neural Information Processing Systems49 citations
Semantic Scholar
Optimally Confident UCB : Improved Regret for Finite-Armed Bandits
Tor Lattimore
2015arXiv.org49 citations
Semantic Scholar
Mirror Descent and the Information Ratio
Tor Lattimore, A. György
2020Annual Conference Computational Learning Theory47 citations
Semantic Scholar
Exploration by Optimisation in Partial Monitoring
Tor Lattimore, Csaba Szepesvari
2019Annual Conference Computational Learning Theory42 citations
Semantic Scholar
General time consistent discounting
Tor Lattimore, Marcus Hutter
2014Theoretical Computer Science41 citations Open access
Semantic Scholar
Asymptotically Optimal Information-Directed Sampling
Johannes Kirschner, Tor Lattimore, Claire Vernade, Csaba Szepesvari
2020Annual Conference Computational Learning Theory40 citations
Semantic Scholar
Thompson Sampling is Asymptotically Optimal in General Environments
Jan Leike, Tor Lattimore, Laurent Orseau, Marcus Hutter
2016Conference on Uncertainty in Artificial Intelligence40 citations
Semantic Scholar
Geometric Entropic Exploration
Z. Guo, M. G. Azar, Alaa Saade, S. Thakoor +6 more
2021arXiv.org39 citations
Semantic Scholar
On the Optimality of Batch Policy Optimization Algorithms
Chenjun Xiao, Yifan Wu, Tor Lattimore, Bo Dai +4 more
2021International Conference on Machine Learning39 citations
Semantic Scholar
Connections Between Mirror Descent, Thompson Sampling and the Information Ratio
Julian Zimmert, Tor Lattimore
2019Neural Information Processing Systems37 citations
Semantic Scholar
Online Learning to Rank with Features
Shuai Li, Tor Lattimore, Csaba Szepesvari
2018International Conference on Machine Learning37 citations
Semantic Scholar
Online Sparse Reinforcement Learning
Botao Hao, Tor Lattimore, Csaba Szepesvari, Mengdi Wang
2020International Conference on Artificial Intelligence and Statistics33 citations
Semantic Scholar
Regret Bounds for Information-Directed Reinforcement Learning
Botao Hao, Tor Lattimore
2022Neural Information Processing Systems32 citations Open access
Semantic Scholar
Cleaning up the neighborhood: A full classification for adversarial partial monitoring
Tor Lattimore, Csaba Szepesvari
2018International Conference on Algorithmic Learning Theory31 citations
Semantic Scholar
Following the Leader and Fast Rates in Linear Prediction: Curved Constraint Sets and Other Regularities
Ruitong Huang, Tor Lattimore, A. György, Csaba Szepesvari
2016Neural Information Processing Systems31 citations
Semantic Scholar
The Pareto Regret Frontier for Bandits
Tor Lattimore
2015Neural Information Processing Systems31 citations
Semantic Scholar
Sparse Feature Selection Makes Batch Reinforcement Learning More Sample Efficient
Botao Hao, Yaqi Duan, Tor Lattimore, Csaba Szepesvari +1 more
2020International Conference on Machine Learning29 citations
Semantic Scholar
Return of the bias: Almost minimax optimal high probability bounds for adversarial linear bandits
Julian Zimmert, Tor Lattimore
2022Annual Conference Computational Learning Theory28 citations
Semantic Scholar
Regret Analysis of the Anytime Optimally Confident UCB Algorithm
Tor Lattimore
2016arXiv.org28 citations
Semantic Scholar
BubbleRank: Safe Online Learning to Re-Rank via Implicit Click Feedback
Chang Li, Branislav Kveton, Tor Lattimore, Ilya Markov +3 more
2018Conference on Uncertainty in Artificial Intelligence27 citations
Semantic Scholar
Soft-Bayes: Prod for Mixtures of Experts with Log-Loss
Laurent Orseau, Tor Lattimore, S. Legg
2017International Conference on Algorithmic Learning Theory26 citations
Semantic Scholar
Online Learning with Gated Linear Networks
J. Veness, Tor Lattimore, Avishkar Bhoopchand, A. Grabska-Barwinska +2 more
2017arXiv.org26 citations
Semantic Scholar
Near-optimal inference in adaptive linear regression
K. Khamaru, Y. Deshpande, Tor Lattimore, L. Mackey +1 more
2021Annals of Statistics24 citations Open access
Semantic Scholar
On First-Order Bounds, Variance and Gap-Dependent Bounds for Adversarial Bandits
Roman Pogodin, Tor Lattimore
2019Conference on Uncertainty in Artificial Intelligence24 citations
Semantic Scholar