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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
12642020
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
1802017
Causal bandits: Learning good interventions via causal inference
F Lattimore, T Lattimore, MD Reid
Advances in Neural Information Processing Systems 29, 2016
160*2016
PAC bounds for discounted MDPs
T Lattimore, M Hutter
International Conference on Algorithmic Learning Theory, 320-334, 2012
1042012
Behaviour suite for reinforcement learning
I Osband, Y Doron, M Hessel, J Aslanides, E Sezener, A Saraiva, ...
arXiv preprint arXiv:1908.03568, 2019
1002019
The end of optimism? an asymptotic analysis of finite-armed linear bandits
T Lattimore, C Szepesvari
Artificial Intelligence and Statistics, 728-737, 2017
932017
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
922020
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
882019
Conservative bandits
Y Wu, R Shariff, T Lattimore, C Szepesvári
International Conference on Machine Learning, 1254-1262, 2016
822016
On explore-then-commit strategies
A Garivier, T Lattimore, E Kaufmann
Advances in Neural Information Processing Systems 29, 2016
712016
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
602019
Bounded Regret for Finite-Armed Structured Bandits
T Lattimore, R Munos
532014
Near-optimal PAC bounds for discounted MDPs
T Lattimore, M Hutter
Theoretical Computer Science 558, 125-143, 2014
522014
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
512019
Toprank: A practical algorithm for online stochastic ranking
T Lattimore, B Kveton, S Li, C Szepesvari
Advances in Neural Information Processing Systems 31, 2018
512018
The sample-complexity of general reinforcement learning
T Lattimore, M Hutter, P Sunehag
International Conference on Machine Learning, 28-36, 2013
512013
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
482020
Universal knowledge-seeking agents for stochastic environments
L Orseau, T Lattimore, M Hutter
International conference on algorithmic learning theory, 158-172, 2013
442013
Refined lower bounds for adversarial bandits
S Gerchinovitz, T Lattimore
Advances in Neural Information Processing Systems 29, 2016
422016
Optimally confident UCB: Improved regret for finite-armed bandits
T Lattimore
arXiv preprint arXiv:1507.07880, 2015
422015
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Articles 1–20