I am Staff Research Scientist in Machine Learning at DeepMind.

I received a Diploma di Laurea in Mathematics from University of Bologna and a PhD in Machine Learning from École Polytechnique Fédérale de Lausanne (IDIAP Research Institute). Before joining DeepMind I worked in the Empirical Inference Department at the Max-Planck Institute for Intelligent Systems (Prof. Dr. Bernhard Schölkopf), in the Machine Intelligence and Perception Group at Microsoft Research Cambridge (Prof. Christopher Bishop) and the Statistical Laboratory, University of Cambridge (Prof. Philip Dawid).

My research interests are based around Bayesian & causal reasoning, graphical models, variational inference, time-series models, deep learning, and ML fairness and bias.

NEW PAPERS:

  • Wasserstein fair classification. R. Jiang, A. Pacchiano, T. Stepleton, H. Jiang, and S. Chiappa, UAI 2019. arXiv:1907.12059
  • Unsupervised separation of dynamics from pixels. S. Chiappa and U. Paquet, METRON, Springer 2019. doi.org/10.1007/s40300-019-00155-4 / arXiv:1907.06430
  • Meta-learning of sequential strategies. P. Ortega et al. 2019. arXiv:1905.03030
  • A causal Bayesian networks viewpoint on fairness. S. Chiappa nd William S. Isaac. In: E. Kosta, J. Pierson, D. Slamanig, S. Fischer-Hübner, S. Krenn (eds) Privacy and Identity Management. Fairness, Accountability, and Transparency in the Age of Big Data. Privacy and Identity 2018. IFIP Advances in Information and Communication Technology, vol 547. Springer, Cham, pages 3-20, 2019. doi.org/10.1007/978-3-030-16744-8_1 / arXiv:1907.06430
  • Degenerate feedback loops in recommender systems. R. Jiang, S. Chiappa, T. Lattimore, A. György, and P. Kohli. ACM Conference on AI, Ethics, and Society, 2019. arXiv:1902.10730
  • Path-specific counterfactual fairness. S. Chiappa. AAAI-2019. [.pdf]
  • Causal reasoning from meta-reinforcement learning. I. Dasgupta, J. Wang, S. Chiappa, J. Mitrovic, P. Ortega, D. Raposo, E. Hughes, P. Battaglia, M. Botvinick, and Z. Kurth-Nelson, 2019. arXiv:1901.08162
  • Comparing interpretable inference models for videos of physical motion. M. Pearce, S. Chiappa, and U. Paquet. AABI 2018.
  • OTHER NEWS:

  • I am Program Chair of AISTATS 2020.
  • I am co-organizing the NeurIPS 2019 workshop Human-Centric ML.
  • Jan Leike and myself gave the ICML 2019 tutorial Safe Machine Learning. The recording is available at SlidesLive.
  • I co-organized the ICLR 2019 Workshop Safe Machine Learning: Specification, Robustness, and Assurance. The recordings is available at SlidesLive.