Curriculum Vitae
NameLocation
EmailTor Lattimore
London, UKtor.lattimore@gmail.com

Education and Academic Positions

2017–present: Senior research scientist at DeepMind

2016–2017: Assistant professor at Indiana University

2014–2016: Postdoctoral fellow at the University of Alberta supervised by Csaba Szepesvári



2010–2013: PhD in Computer Science with Marcus Hutter at the Australian National University

2006–2009: Bachelor of Science (Advanced), 1st class honours in Mathematics at the Australian NationalUniversityPublications[1] Branislav Kveton, Csaba Szepesvári, Zheng Wen, Mohammad Ghavamzadeh, and Tor Lattimore. Garbage
in, reward out: Bootstrapping exploration in multi-armed bandits. In Proceedings of the 36th International
Conference on Machine Learning. 2019.
[2] Tor Lattimore and Csaba Szepesvári. Bandit Algorithms. Cambridge University Press (draft), 2019.
[3] Tor Lattimore and Csaba Szepesvári. Cleaning up the neighborhood: A full classification for adversarial partial
monitoring. In Aurélien Garivier and Satyen Kale, editors, Proceedings of the 30th International Conferenceon Algorithmic Learning Theory, volume 98 of Proceedings of Machine Learning Research, pages 529–556.
PMLR, 2019.
[4] Tor Lattimore and Csaba Szepesvári. An information-theoretic approach to minimax regret in partial monitoring. In Conference on Learning Theory. 2019.
[5] Shuai Li, Tor Lattimore, and Csaba Szepesvári. Online learning to rank with features. In Proceedings of the36th International Conference on Machine Learning. 2019.
[6] Branislav Kveton, Chang Li, Tor Lattimore, Ilya Markov, Maarten de Rijke, Csaba Szepesvári, and Masrour
Zoghi. Bubblerank: Safe online learning to rerank. arXiv preprint, 2018.
[7] Tor Lattimore. Refining the confidence level for optimistic bandit strategies. Journal of Machine Learning
Research, 19(20):1–32, 2018.
[8] Tor Lattimore, Branislav Kveton, Shuai Li, and Csaba Szepesvári. Toprank: A practical algorithm for online
stochastic ranking. In Proceedings of the 31st Conference on Neural Information Processing Systems. 2018.
[9] Laurent Orseau, Levi Lelis, Tor Lattimore, and Theophane Weber. Single-agent policy tree search withguarantees. In Proceedings of the 31st Conference on Neural Information Processing Systems. 2018.
[10] Christoph Dann, Tor Lattimore, and Emma Brunskill. Unifying pac and regret: Uniform pac bounds for
episodic reinforcement learning. In Proceedings of the 30th Conference on Neural Information ProcessingSystems. 2017.

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[11] Ruitong Huang, Tor Lattimore, András György, and Csaba Szepesvári. Following the leader and fast rates inonline linear prediction: Curved constraint sets and other regularities. Journal of Machine Learning Research,
18(145):1–31, 2017.
[12] Tor Lattimore. A scale free algorithm for stochastic bandits with bounded kurtosis. In Proceedings of the30th Conference on Neural Information Processing Systems. 2017.
[13] Tor Lattimore and Csaba Szepesvari. The End of Optimism? An Asymptotic Analysis of Finite-Armed LinearBandits. In Aarti Singh and Jerry Zhu, editors, Proceedings of the 20th International Conference on Artificial
Intelligence and Statistics, volume 54 of Proceedings of Machine Learning Research, pages 728–737. PMLR,Fort Lauderdale, FL, USA, 20–22 Apr 2017.
[14] Laurent Orseau, Tor Lattimore, and Shane Legg. Soft-bayes: Prod for mixtures of experts with log-loss. In
Proceedings of the 28th International Conference on Algorithmic Learning Theory. 2017.
[15] Joel Veness, Tor Lattimore, Avishkar Bhoopchand, Agnieszka Grabska-Barwinska, Christopher Mattern, andPeter Toth. Online learning with gated linear networks. Technical report, 2017.
[16] Aurélien Garivier, Emilie Kaufmann, and Tor Lattimore. On explore-then-commit strategies. In Proceedingsof the 29th Conference on Neural Information Processing Systems (NIPS). 2016.
[17] Sébastien Gerchinovitz and Tor Lattimore. Refined lower bounds for adversarial bandits. In Proceedings ofthe 29th Conference on Neural Information Processing Systems (NIPS). 2016.
[18] Ruitong Huang, Tor Lattimore, András Gyögy, and Csaba Szepesvári. Following the leader and fast rates inlinear prediction: Curved constraint sets and other regularities. In Proceedings of the 29th Conference on
Neural Information Processing Systems (NIPS). 2016.
[19] Finnian Lattimore, Tor Lattimore, and Mark Reid. Causal bandits: Learning good interventions via causal
inference. In Proceedings of the 29th Conference on Neural Information Processing Systems (NIPS). 2016.
[20] Tor Lattimore. Regret analysis of the anytime optimally confident UCB algorithm. Technical report, 2016.
[21] Tor Lattimore. Regret analysis of the finite-horizon Gittins index strategy for multi-armed bandits. InProceedings of Conference On Learning Theory (COLT). 2016.
[22] Jan Leike, Tor Lattimore, Laurent Orseau, and Marcus Hutter. Thompson sampling is asymptotically optimalin general environments. In Proceedings of the 32nd Conference on Uncertainty in Artificial Intelligence (UAI).
2016.
[23] Yifan Wu, Roshan Shariff, Tor Lattimore, and Csaba Szepesvári. Conservative bandits. In Proceedings ofthe International Conference on Machine Learning (ICML). 2016.
[24] Tor Lattimore. Optimally confident UCB : Improved regret for finite-armed bandits. Technical report, 2015.
[25] Tor Lattimore. The pareto regret frontier for bandits. In Proceedings of the 28th Conference on NeuralInformation Processing Systems (NIPS). 2015.
[26] Tor Lattimore, Koby Crammer, and Csaba Szepesvári. Linear multi-resource allocation with semi-banditfeedback. In Proceedings of the 28th Conference on Neural Information Processing Systems (NIPS). 2015.
[27] Tom Everitt, Tor Lattimore, and Marcus Hutter. Free lunch for optimisation under the universal distribution.
In Proceedings of IEEE Congress on Evolutionary Computing (CEC). 2014.
[28] Tor Lattimore, Koby Crammer, and Csaba Szepesvári. Optimal resource allocation with semi-bandit feedback.
In Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence (UAI). 2014.

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[29] Tor Lattimore, András György, and Csaba Szepesvári. On learning the optimal waiting time. In Proceedingsof the 25th Conference on Algorithmic Learning Theory (ALT). 2014.
[30] Tor Lattimore and Marcus Hutter. Asymptotics of continuous Bayes for non-i.i.d. sources. Technical report,
2014.
[31] Tor Lattimore and Marcus Hutter. Bayesian reinforcement learning with exploration. In Proceedings of the25th Conference on Algorithmic Learning Theory (ALT). 2014.
[32] Tor Lattimore and Marcus Hutter. General time consistent discounting. Theoretical Computer Science,519(0):140 – 154, 2014.
[33] Tor Lattimore and Marcus Hutter. On Martin-löf (non-)convergence of Solomonoff’s universal mixture.
Theoretical Computer Science, 2014.
[34] Tor Lattimore and Rémi Munos. Bounded regret for finite-armed structured bandits. In Proceedings of the27th Conference on Neural Information Processing Systems (NIPS). 2014.
[35] Tor Lattimore and Marcus Hutter. No free lunch versus Occam’s razor in supervised learning. In David Dowe,editor, Algorithmic Probability and Friends. Bayesian Prediction and Artificial Intelligence, volume 7070 ofLecture Notes in Computer Science, pages 223–235. Springer Berlin Heidelberg, 2013.
[36] Tor Lattimore and Marcus Hutter. On Martin-Löf convergence of Solomonoff’s mixture. In T-H.HubertChan, LapChi Lau, and Luca Trevisan, editors, Theory and Applications of Models of Computation, volume7876 of Lecture Notes in Computer Science, pages 212–223. Springer Berlin Heidelberg, 2013.
[37] Tor Lattimore, Marcus Hutter, and Peter Sunehag. Concentration and confidence for discrete bayesian sequence predictors. In Sanjay Jain, Rémi Munos, Frank Stephan, and Thomas Zeugmann, editors, Proceedingsof the 24th International Conference on Algorithmic Learning Theory, pages 324–338. Springer, 2013.
[38] Tor Lattimore, Marcus Hutter, and Peter Sunehag. The sample-complexity of general reinforcement learning.
In Proceedings of the 30th International Conference on Machine Learning. 2013.
[39] Laurent Orseau, Tor Lattimore, and Marcus Hutter. Universal knowledge-seeking agents for stochasticenvironments. In Sanjay Jain, Rémi Munos, Frank Stephan, and Thomas Zeugmann, editors, Proceedingsof the 24th International Conference on Algorithmic Learning Theory, volume 8139 of Lecture Notes inComputer Science, pages 158–172. Springer Berlin Heidelberg, 2013.
[40] Tor Lattimore and Marcus Hutter. PAC bounds for discounted MDPs. In Nader Bshouty, Gilles Stoltz, Nicolas
Vayatis, and Thomas Zeugmann, editors, Proceedings of the 23th International Conference on Algorithmic
Learning Theory, volume 7568 of Lecture Notes in Computer Science, pages 320–334. Springer Berlin /Heidelberg, 2012.
[41] Tor Lattimore and Marcus Hutter. Asymptotically optimal agents. In Jyrki Kivinen, Csaba Szepesvári, Esko
Ukkonen, and Thomas Zeugmann, editors, Proceedings of the 22nd International Conference on Algorithmic
Learning Theory, volume 6925 of Lecture Notes in Computer Science, pages 368–382. Springer Berlin /Heidelberg, 2011.
[42] Tor Lattimore and Marcus Hutter. Time consistent discounting. In Jyrki Kivinen, Csaba Szepesvári, EskoUkkonen, and Thomas Zeugmann, editors, Proceedings of the 22nd International Conference on Algorithmic
Learning Theory, volume 6925 of Lecture Notes in Computer Science, pages 383–397. Springer Berlin /Heidelberg, 2011.
[43] Tor Lattimore, Marcus Hutter, and Vaibhav Gavane. Universal prediction of selected bits. In Jyrki Kivinen,Csaba Szepesvári, Esko Ukkonen, and Thomas Zeugmann, editors, Proceedings of the 22nd International
Conference on Algorithmic Learning Theory, volume 6925 of Lecture Notes in Computer Science, pages
262–276. Springer Berlin / Heidelberg, 2011.
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Invited Talks

SequeL machine learning group, Lille. 2018

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Data Learning and Inference (DALI). Auto-tuning stochastic bandits. 2016

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4th Rutgers Applied Probability Conference. Presenting the Optimally Confident UCB. 2015

Microsoft Research. Presenting Optimal Resource Allocation with Semi-Bandit Feedback. 2014

Prizes and Grants


UAI 2016. Best student paper award [22] (Jan as student)

ICML 2015. Reviewer Award

UAI 2014. Runner-up best paper award [28]

Google Australia Fellowship in Machine Learning ($15 000), 2013

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Vice-Chancellor’s HDR Travel Grant ($1 000), 2012

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PASCAL Internal Visiting Programme (AC1 000), 2012

Boyapati Computer Science & Mathematical Honours Scholarship ($5 000), 2009

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3rd year Boyapati Computer Science & Mathematics Prize, 2008 (joint winner)

2st year Boyapati Computer Science & Mathematics Prize, 2007 (joint winner)

1st year Boyapati Computer Science & Mathematics Prize, 2006 (joint winner)

1st place, UNSW Programming Competition, 2005 (with Alex Osborne and Chris Pelling)

3rd place, UNSW Programming Competition, 2004 (with Alex Osborne and Chris Pelling)

TeachingLecturer

B551: Introduction to AI, Indiana University 2016 ( 60 masters students)

Tutorials


AAAI, 2018 (on bandits with Csaba Szepesvári)

Oxford statistics group. Tutorial on bandit algorithms, 2018

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Reinforcement learning summer school (on bandit algorithms and exploration). Toronto, 2018

European Workshop on Reinforcement Learning (bandits). Lille, 2018

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Winter School on Learning and Artificial intelligence (bandits). Trieste, 2018

Gatsby Computational Neuroscience Unit (bandits), 2018

Imperial College London machine learning tutorials (bandits), 2018
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Teaching assistant

MATH1014 (Introduction to Linear Algebra)

MATH2306 (Partial Differential Equations and Applications)

COMP6467 (Statistical Machine Learning)

COMP4620/COMP8620 (Advanced Artificial Intelligence)

Reviewing
I have been on the program committee for the following conferences:Artificial Intelligence and Statistics Conference (AISTATS), 2017, 2018

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Algorithmic Learning Theory (ALT), 2017, 2019

Neural Information Processing Systems (NIPS), 2016, 2017, 2018

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AAAI Conference on Artificial Intelligence (AAAI), 2016

International Joint Conference on Artificial Intelligence (IJCAI), 2015

International Conference on Machine Learning (ICML), 2015

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Artificial General Intelligence (AGI), 2014

I have reviewed articles for a number of conferences and journals including:Conference on Learning Theory (COLT)Journal of Machine Learning Research (JMLR)

Machine Learning (ML)Mathematics of Operations Research (MOR)

Uncertainty in Artificial Intelligence (UAI)IEEE Transactions on Automatic Control (TAC)

IEEE Transactions on Information Theory (TIT)

Neural Information Processing Systems (NIPS)International Conference on Artificial Intelligence and Statistics (AISTATS)

Theoretical Computer Science (TCS)European Conference on Machine Learning (ECML)

Algorithmic Learning Theory (ALT)European Workshop on Reinforcement Learning (EWRL)

Artificial General Intelligence (AGI)

5Outreach“Lunch and Learn” presentation for year 11/12 students at the University of AlbertaCo-supervising four high school students over two summers for the University of Alberta’s internship program

Other skills
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C/C++, Ruby, Python

SQL

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Joint author of school timetabling application https://prophetapp.com.au (in use by most colleges inthe Australian Capital Territory)

References
Csaba Szepesvári (postdoctoral supervisor at the University of Alberta, szepesva@ualberta.ca)

Marcus Hutter (PhD supervisor at the Australian National University, marcus.hutter@anu.edu.au)Rémi Munos (collaborator at Google DeepMind/INRIA Lille, remi.munos@inria.fr)

András György (collaborator at University of Alberta, a.gyorgy@imperial.ac.uk)

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