Tom Schaul

Tom Schaul, Ph.D.

I am postdoc in machine learning at the Courant Institute of NYU, in the lab of Yann LeCun, focusing on learning from temporal sequences. I will shortly be joining DeepMind Inc. in London.

My research interests include (modular) reinforcement learning, stochastic (black-box) optimization with minimal hyperparameter tuning, and (deep/recurrent) neural networks. I use these techniques in application domains ranging from computer vision and neural decoding to games and robotics.

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.

Selected papers

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]
ICLR 2013 T. Schaul, Y. LeCun. Adaptive Learning Rates and Parallelization for Stochastic, Sparse, Non-smooth Gradients.
International Conference on Learning Representations. [Pdf] [Code] [Public reviews] [BibTeX]
CIG 2013 T. Schaul. A Video Game Description Language for Model-based or Interactive Learning.
IEEE Conference on Computational Intelligence in Games. [Pdf] [Code] [BibTeX]
GECCO 2012 T. Schaul. Natural Evolution Strategies Converge on Sphere Functions.
Genetic and Evolutionary Computation Conference. [Pdf] [BibTeX]
IJCAI 2011 M. Ring, T. Schaul. Q-error as a Selection Mechanism in Modular Reinforcement-Learning Systems.
International Joint Conference on Artificial Intelligence. [Pdf] [BibTeX]
JMLR 2010 T. Schaul, J. Bayer, D. Wierstra, Y. Sun, M. Felder, F. Sehnke, T. Rückstieß, J. Schmidhuber. PyBrain.
Journal of Machine Learning Research. [Pdf] [BibTeX]
ICML 2009 Y. Sun, D. Wierstra, T. Schaul, J. Schmidhuber. Stochastic Search using the Natural Gradient.
International Conference on Machine Learning. [Pdf] [BibTeX]
See the full list of publications for more.

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