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Tor Lattimore
Tor Lattimore
DeepMind
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Title
Cited by
Cited by
Year
Bandit algorithms
T Lattimore, C Szepesvári
Cambridge University Press, 2020
18742020
Unifying PAC and regret: Uniform PAC bounds for episodic reinforcement learning
C Dann, T Lattimore, E Brunskill
Advances in Neural Information Processing Systems 30, 2017
2442017
Causal bandits: Learning good interventions via causal inference
F Lattimore, T Lattimore, MD Reid
Advances in Neural Information Processing Systems 29, 2016
196*2016
Degenerate feedback loops in recommender systems
R Jiang, S Chiappa, T Lattimore, A György, P Kohli
Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, 383-390, 2019
1542019
Learning with good feature representations in bandits and in rl with a generative model
T Lattimore, C Szepesvari, G Weisz
International Conference on Machine Learning, 5662-5670, 2020
1342020
Behaviour suite for reinforcement learning
I Osband, Y Doron, M Hessel, J Aslanides, E Sezener, A Saraiva, ...
arXiv preprint arXiv:1908.03568, 2019
1292019
PAC bounds for discounted MDPs
T Lattimore, M Hutter
Algorithmic Learning Theory: 23rd International Conference, ALT 2012, Lyon …, 2012
1222012
The end of optimism? an asymptotic analysis of finite-armed linear bandits
T Lattimore, C Szepesvari
Artificial Intelligence and Statistics, 728-737, 2017
1132017
Conservative bandits
Y Wu, R Shariff, T Lattimore, C Szepesvári
International Conference on Machine Learning, 1254-1262, 2016
1002016
On explore-then-commit strategies
A Garivier, T Lattimore, E Kaufmann
Advances in Neural Information Processing Systems 29, 2016
922016
A geometric perspective on optimal representations for reinforcement learning
M Bellemare, W Dabney, R Dadashi, A Ali Taiga, PS Castro, N Le Roux, ...
Advances in neural information processing systems 32, 2019
792019
Model selection in contextual stochastic bandit problems
A Pacchiano, M Phan, Y Abbasi Yadkori, A Rao, J Zimmert, T Lattimore, ...
Advances in Neural Information Processing Systems 33, 10328-10337, 2020
712020
Bounded Regret for Finite-Armed Structured Bandits
T Lattimore, R Munos
642014
Near-optimal PAC bounds for discounted MDPs
T Lattimore, M Hutter
Theoretical Computer Science 558, 125-143, 2014
632014
Toprank: A practical algorithm for online stochastic ranking
T Lattimore, B Kveton, S Li, C Szepesvari
Advances in Neural Information Processing Systems 31, 2018
622018
The sample-complexity of general reinforcement learning
T Lattimore, M Hutter, P Sunehag
International Conference on Machine Learning, 28-36, 2013
612013
Garbage in, reward out: Bootstrapping exploration in multi-armed bandits
B Kveton, C Szepesvari, S Vaswani, Z Wen, T Lattimore, M Ghavamzadeh
International Conference on Machine Learning, 3601-3610, 2019
602019
Refined lower bounds for adversarial bandits
S Gerchinovitz, T Lattimore
Advances in Neural Information Processing Systems 29, 2016
512016
Linear multi-resource allocation with semi-bandit feedback
T Lattimore, K Crammer, C Szepesvári
Advances in Neural Information Processing Systems 28, 2015
482015
An information-theoretic approach to minimax regret in partial monitoring
T Lattimore, C Szepesvári
Conference on Learning Theory, 2111-2139, 2019
462019
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