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Léon Bottou


  • Fellow
  • Learning in Machines & Brains




The long-term goal of Léon Bottou’s research is to understand how to replicate human-level intelligence.

Because this goal requires conceptual advances that cannot be anticipated, Leon’s research has followed many practical and theoretical turns: neural networks applications in the late 1980s, stochastic gradient learning algorithms and statistical properties of learning systems in the early 1990s, computer vision applications with structured outputs in the late 1990s, theory of large scale learning in the 2000s. During the last few years, Léon Bottou’s research aims to clarify the relation between learning and reasoning, with more and more focus on the many aspects of causation (inference, invariance, reasoning, affordance, and intuition.)


  • Blavatnik Award for Young Scientists, 2007
  • NeurIPS Test of Time Award: “The Tradeoffs of Large-Scale Learning", 2018

Relevant Publications

  • Martin Arjovsky, S. C., & Bottou, L. (2017, August). Wasserstein generative adversarial networks. In Proceedings of the 34th International Conference on Machine Learning, Sydney, Australia.
  • Bottou, L., Peters, J., Quiñonero-Candela, J., Charles, D. X., Chickering, D. M., Portugaly, E., ... & Snelson, E. (2013). Counterfactual reasoning and learning systems: The example of computational advertising. The Journal of Machine Learning Research, 14(1), 3207-3260.
  • Bottou, L., & Bousquet, O. (2008). The tradeoffs of large scale learning. In Advances in neural information processing systems (pp. 161-168).
  • Bordes, A., Ertekin, S., Weston, J., & Bottou, L. (2005). Fast kernel classifiers with online and active learning. Journal of Machine Learning Research, 6(Sep), 1579-1619.
  • LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278-2324.

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