Tor Lattimore

Home * People * Tor Lattimore



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, which participated at various Australasian National Computer Chess Championship and CCT Tournaments.

=Selected Publications=

2010 ...

 * Tor Lattimore, Marcus Hutter, Vaibhav Gavane (2011). Universal Prediction of Selected Bits. Algorithmic Learning Theory, Lecture Notes in Computer Science 6925, Springer, arXiv:1107.5531
 * Tor Lattimore, Marcus Hutter (2011). Asymptotically Optimal Agents. Algorithmic Learning Theory, Lecture Notes in Computer Science 6925, Springer
 * Tor Lattimore, Marcus Hutter (2011). Time Consistent Discounting. Algorithmic Learning Theory, Lecture Notes in Computer Science 6925, Springer, arXiv:1107.5528
 * Tor Lattimore, Marcus Hutter (2011). No Free Lunch versus Occam's Razor in Supervised Learning. Solomonoff Memorial, Lecture Notes in Computer Science, Springer, arXiv:1111.3846
 * Tor Lattimore, Marcus Hutter (2012). PAC Bounds for Discounted MDPs. Algorithmic Learning Theory, Lecture Notes in Computer Science, Springer
 * Tor Lattimore, Marcus Hutter (2014). Bayesian Reinforcement Learning with Exploration. Algorithmic Learning Theory, Lecture Notes in Computer Science 8776, Springer
 * Tor Lattimore, Remi Munos (2014). Bounded Regret for Finite-Armed Structured Bandits. arXiv:1411.2919

2015 ...
=Forum Posts=
 * 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:1712.0189
 * 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
 * pawn hash by Tor Lattimore, CCC, July 03, 2004 » Pawn Hash Table
 * MTD Drivers by Tor Lattimore, CCC, August 10, 2004 » MTD(f)
 * Qsearch Checks by Tor Lattimore, CCC, August 29, 2004 » Quiescence Search, Check

=External Links=
 * Tor Lattimore - Homepage
 * Bandit Algorithms


 * tor (Tor Lattimore) · GitHub
 * Tor Lattimore - Google+
 * Lattimore, Tor FIDE Chess Profile
 * Tor Lattimore chess games - 365Chess.com
 * chessexpress: Ow, my brain hurts by Shaun Press, October 03, 2007

=References= Up one level