Tom Schaul

Tom Schaul, Ph.D.

I am a senior research scientist at Google DeepMind in London.

My research interests include (modular/hierarchical) reinforcement learning, (stochastic/black-box) optimization with minimal hyperparameter tuning, and (deep/recurrent) neural networks. My favorite application domain are games.

I grew up in Luxembourg and studied computer science in Switzerland (with exchanges at Waterloo and Columbia), where I obtained an MSc from the EPFL in 2005. I hold a PhD from TU Munich (2011), which I did under the supervision of Jürgen Schmidhuber at the Swiss AI Lab IDSIA. From 2011 to 2013 I was a postdoc at the Courant Institute of NYU, in the lab of Yann LeCun.

Selected papers

arXiv 2016

D. Silver, H. van Hasselt, M. Hessel, T. Schaul, A. Guez et al.
The Predictron: End-To-End Learning and Planning. [arXiv]

ICLR 2016

T. Schaul, J. Quan, I. Antonoglou and D. Silver. Prioritized Experience Replay.
International Conference on Learning Representations. [arXiv][BibTeX]

ICML 2015

T. Schaul, D. Horgan, K. Gregor and D. Silver. Universal Value Function Approximators.
International Conference on Machine Learning. [Pdf] [BibTeX]

JMLR 2014

D. Wierstra, T. Schaul, T. Glasmachers, Y. Sun, J. Peters, J. Schmidhuber. Natural Evolution Strategies.
Journal of Machine Learning Research. [Pdf] [BibTeX]

ICML 2013 T. Schaul, S. Zhang, Y. LeCun. No more Pesky Learning Rates.
International Conference on Machine Learning. [Pdf] [Supplementary material] [Code] [BibTeX]
IJCAI 2013 T. Schaul, M. Ring. Better Generalization with Forecasts.
International Joint Conference on Artificial Intelligence. [Pdf] [Slides] [BibTeX]
See the full list of publications for 40+ additional ones.

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