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.
|ICLR 2019||D. Borsa, A. Barreto, J. Quan, D. Mankowitz, H. van Hasselt, R. Munos, D. Silver and T. Schaul.
Universal Successor Features Approximators.
International Conference on Learning Representations. [arXiv]
D. Silver, H. van Hasselt, M. Hessel, T. Schaul, A. Guez et al. The Predictron: End-To-End Learning and Planning.
|ICML 2013||T. Schaul, S. Zhang, Y. LeCun. No more Pesky Learning Rates.
International Conference on Machine Learning. [Pdf] [Supplementary material] [Code] [BibTeX]