Tor Lattimore

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Tor Lattimore [1] Tor Lattimore,

an Australian computer scientist and since 2017 research scientist at DeepMind in London, Ph.D. in 2013 with Marcus Hutter at Australian National University, and postdoc at University of Alberta supervised by Csaba Szepesvári. His research interests include various machine learning topics and optimization problems, in particular reinforcement learning, probably approximately correct learning in Markov decision processes and multi-armed bandit problems. As a chess player, and former computer chess programmer, Tor Lattimore is author of the Chess Engine Communication Protocol compatible chess engine SEE [2], which participated at various Australasian National Computer Chess Championship and CCT Tournaments.

Tor Lattimore (2015). Optimally Confident UCB: Improved Regret for Finite-Armed Bandits. arXiv:1507.07880
Tor Lattimore (2016). Regret Analysis of the Anytime Optimally Confident UCB Algorithm. arXiv:1603.08661
Tom Everitt, Tor Lattimore, Marcus Hutter (2016). Free Lunch for Optimisation under the Universal Distribution. arXiv:1608.04544
Tor Lattimore, Csaba Szepesvári (2017). The End of Optimism? An Asymptotic Analysis of Finite-Armed Linear Bandits. AISTATS, pdf, arXiv:1610.04491 (2016)
Joel Veness, Tor Lattimore, Avishkar Bhoopchand, Agnieszka Grabska-Barwinska, Christopher Mattern, Peter Toth (2017). Online Learning with Gated Linear Networks. arXiv:1712.01897
Tor Lattimore, Csaba Szepesvári (2018). Cleaning up the neighborhood: A full classification for adversarial partial monitoring. arXiv:1805.09247
Tor Lattimore, Csaba Szepesvári (2019). Bandit Algorithms. Cambridge University Press (draft), pdf