Tor Lattimore
Tor Lattimore
DeepMind
Verified email at google.com - Homepage
Title
Cited by
Cited by
Year
Bandit algorithms
T Lattimore, C Szepesvári
Cambridge University Press, 2020
7322020
Unifying PAC and regret: Uniform PAC bounds for episodic reinforcement learning
C Dann, T Lattimore, E Brunskill
arXiv preprint arXiv:1703.07710, 2017
1312017
Optimal cluster recovery in the labeled stochastic block model
SY Yun, A Proutiere
Advances in Neural Information Processing Systems 29, 965-973, 2016
122*2016
PAC bounds for discounted MDPs
T Lattimore, M Hutter
International Conference on Algorithmic Learning Theory, 320-334, 2012
852012
The end of optimism? an asymptotic analysis of finite-armed linear bandits
T Lattimore, C Szepesvari
Artificial Intelligence and Statistics, 728-737, 2017
752017
Behaviour suite for reinforcement learning
I Osband, Y Doron, M Hessel, J Aslanides, E Sezener, A Saraiva, ...
arXiv preprint arXiv:1908.03568, 2019
642019
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
592020
Conservative bandits
Y Wu, R Shariff, T Lattimore, C Szepesvári
International Conference on Machine Learning, 1254-1262, 2016
592016
On explore-then-commit strategies
A Garivier, T Lattimore, E Kaufmann
Advances in Neural Information Processing Systems 29, 784-792, 2016
562016
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
462019
Near-optimal PAC bounds for discounted MDPs
T Lattimore, M Hutter
Theoretical Computer Science 558, 125-143, 2014
422014
Universal knowledge-seeking agents for stochastic environments
L Orseau, T Lattimore, M Hutter
International conference on algorithmic learning theory, 158-172, 2013
422013
The sample-complexity of general reinforcement learning
T Lattimore, M Hutter, P Sunehag
International Conference on Machine Learning, 28-36, 2013
422013
Bounded Regret for Finite-Armed Structured Bandits
T Lattimore, R Munos
402014
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, 4358-4369, 2019
392019
Toprank: A practical algorithm for online stochastic ranking
T Lattimore, B Kveton, S Li, C Szepesvari
arXiv preprint arXiv:1806.02248, 2018
372018
Refined lower bounds for adversarial bandits
S Gerchinovitz, T Lattimore
Advances in Neural Information Processing Systems, 1198-1206, 2016
362016
General time consistent discounting
T Lattimore, M Hutter
Theoretical Computer Science 519, 140-154, 2014
362014
No free lunch versus Occam’s razor in supervised learning
T Lattimore, M Hutter
Algorithmic Probability and Friends. Bayesian Prediction and Artificial …, 2013
362013
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
352019
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