BibTex publications.bib

• 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:1907.12906




Meta-learning of sequential strategies.
P. A. Ortega, J. X. Wang, M. Rowland, T. Genewein, Z. Kurth-Nelson, R. Pascanu, N. Heess, J. Veness, A. Pritzel, P. Sprechmann, S. M. Jayakumar, T. McGrath, K. Miller, M. Azar, I. Osband, N. Rabinowitz, A. György, S. Chiappa S. Osindero, Y. W. Teh, H. van Hasselt, N. de Freitas, M. Botvinick, and S. Legg, 2019. arXiv:1905.03030




A causal Bayesian networks viewpoint on fairness.
S. Chiappa and 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. Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19), pages 7801-7808, 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, 2018. [.pdf]



• Recurrent environment simulators.
  S. Chiappa, S. Racaniere, D. Wierstra, and S. Mohamed. In Fifth International Conference on Learning Representations (ICLR), pages 1-61, 2017. [.pdf]




Inference and learning in latent Markov models.
D. Barber and S. Chiappa. Advanced State Space Methods for Neural and Clinical Data. Z. Chen Editor, Cambridge University Press, pages 14-50, 2015.




Explicit-duration Markov switching models.
S. Chiappa. Foundations and Trends in Machine Learning, 7(6): pages 803-886, 2014. [.pdf]



• A probabilistic model of biological ageing of the lungs for analysing the effects of smoking, asthma and COPD.
  S. Chiappa, J. Winn, A. Vinuela, H. Tipney, and T. D. Spector. Respiratory Research, 14:60, 2013. [.pdf] DOI: 10.1186/1465-9921-14-60 http://respiratory-research.com/content/14/1/60.




Bayesian Time Series Models, D. Barber, A. T. Cemgil, and S. Chiappa Editors, Cambridge University Press, pages 1-432, August 2011. [.pdf]



• Inference and estimation in probabilistic time series models.
  D. Barber, A. T. Cemgil, and S. Chiappa. Bayesian Time Series Models. D. Barber, A. T. Cemgil, and S. Chiappa Editors, Cambridge University Press, pages 1-31, 2011. [.pdf]



• Movement extraction by detecting dynamics switches and repetitions.
  S. Chiappa and J. Peters. In Advances in Neural Information Processing Systems 23 (NIPS 2010), pages 388-396, 2010. [.pdf]



• A Bayesian approach to graph regression with relevant subgraph selection.
  S. Chiappa, H. Saigo, and K. Tsuda. In 9th SIAM International Conference on Data Mining (SDM), pages 295-304, 2009. [.pdf]



• Using Bayesian dynamical systems for motion template libraries.
  S. Chiappa, J. Kober, and J. Peters. In Advances in Neural Information Processing Systems 21 (NIPS 2008), pages 297-304, 2009. [.pdf] (A video with executions of the ball-in-a-cup game of dexterity by an anthropomorphic SARCOS arm is available here)



• A Bayesian approach to switching linear Gaussian state-space models for unsupervised time-series segmentation.
  S. Chiappa. In 7th International Conference on Machine Learning and Applications (ICMLA), pages 3-9, 2008. [.pdf]



• Output grouping using Dirichlet mixtures of linear Gaussian state-space models.
  S. Chiappa and D. Barber. In 5th IEEE International Symposium on Image and Signal Processing and Analysis (ISPA), pages 446-451, 2007. [.pdf]



• Unified inference for variational Bayesian linear Gaussian state-space models.
  D. Barber and S. Chiappa. In Advances in Neural Information Processing Systems 19 (NIPS 2006), pages 81-88, 2007. [.pdf] (corrected version of the proceedings publication: in Algorithm 1 U_AB has been replaced with its transpose and viceversa) (Matlab code)



• Bayesian factorial linear Gaussian state-space models for biosignal decomposition.
  S. Chiappa and D. Barber. Signal Processing Letters, 14(4): pages 267-270, 2007. [.pdf] (Matlab code)



• Analysis and Classification of EEG Signals using Probabilistic Models for Brain Computer Interfaces.
  S. Chiappa. Ph.D. thesis 3547, EPF Lausanne, Switzerland, pages 1-131, 2006. [.pdf]



• EEG classification using generative independent component analysis.
  S. Chiappa and D. Barber. Neurocomputing, 69(7-9): pages 769-777, 2006. [.pdf]



• Generative independent component analysis for EEG classification.
  S. Chiappa and D. Barber. In 13th European Symposium on Artificial Neural Networks (ESANN), pages 297-302, 2005. [.pdf]



• Generative temporal ICA for classification in asynchronous BCI systems.
  S. Chiappa and D. Barber. In 2nd International IEEE EMBS Conference on Neural Engineering, pages 514-517, 2005. [.pdf]



• HMM and IOHMM modeling of EEG rhythms for asynchronous BCI systems.
  S. Chiappa and S. Bengio. In 12th European Symposium on Artificial Neural Networks (ESANN), pages 199-204, 2004. [.pdf]



• Evolution of the mental states operating a brain-computer interface.
  J. Mouriño, S. Chiappa, R. Jané, and José del R. Millán. In 24th Annual EMBS International Conference, pages 400-401, 2002.



• Spatial filtering in the training process of a brain computer interface.
  J. Mouriño, José del R. Millán, F. Cincotti, S. Chiappa , R. Jané, and F. Babiloni. 4In 23rd Annual EMBS International Conference, pages 639-642, 2001. [.pdf]