Silvia Chiappa

According to our database, Silvia Chiappa authored at least 35 papers between 2004 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2023

DiscoGen: Learning to Discover Gene Regulatory Networks.

Nan Rosemary Ke, Sara-Jane Dunn, Jörg Bornschein, Silvia Chiappa, Mélanie Rey, Jean-Baptiste Lespiau, Albin Cassirer, Jane X. Wang, Theophane Weber, David G. T. Barrett, Matthew M. Botvinick, Anirudh Goyal, Michael Mozer, Danilo J. Rezende

CoRR, 2023

Pragmatic Fairness: Developing Policies with Outcome Disparity Control.

Limor Gultchin, Siyuan Guo, Alan Malek, Silvia Chiappa, Ricardo Silva

CoRR, 2023

Functional causal Bayesian optimization.

Limor Gultchin, Virginia Aglietti, Alexis Bellot, Silvia Chiappa

Proceedings of the Uncertainty in Artificial Intelligence, 2023

Transportability for Bandits with Data from Different Environments.

Alexis Bellot, Alan Malek, Silvia Chiappa

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Additive Causal Bandits with Unknown Graph.

Alan Malek, Virginia Aglietti, Silvia Chiappa

Proceedings of the International Conference on Machine Learning, 2023

Constrained Causal Bayesian Optimization.

Virginia Aglietti, Alan Malek, Ira Ktena, Silvia Chiappa

Proceedings of the International Conference on Machine Learning, 2023

Learning to Induce Causal Structure.

Nan Rosemary Ke, Silvia Chiappa, Jane X. Wang, Jörg Bornschein, Anirudh Goyal, Mélanie Rey, Theophane Weber, Matthew M. Botvinick, Michael Curtis Mozer, Danilo Jimenez Rezende

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Learning to Induce Causal Structure.

Nan Rosemary Ke, Silvia Chiappa, Jane Wang, Jörg Bornschein, Theophane Weber, Anirudh Goyal, Matthew M. Botvinick, Michael Mozer, Danilo Jimenez Rezende

CoRR, 2022

Maintaining fairness across distribution shift: do we have viable solutions for real-world applications?

Jessica Schrouff, Natalie Harris, Oluwasanmi Koyejo, Ibrahim Alabdulmohsin, Eva Schnider, Krista Opsahl-Ong, Alexander Brown, Subhrajit Roy, Diana Mincu, Christina Chen, Awa Dieng, Yuan Liu, Vivek Natarajan, Alan Karthikesalingam, Katherine A. Heller, Silvia Chiappa, Alexander D'Amour

CoRR, 2022

Diagnosing failures of fairness transfer across distribution shift in real-world medical settings.

Jessica Schrouff, Natalie Harris, Sanmi Koyejo, Ibrahim M. Alabdulmohsin, Eva Schnider, Krista Opsahl-Ong, Alexander Brown, Subhrajit Roy, Diana Mincu, Christina Chen, Awa Dieng, Yuan Liu, Vivek Natarajan, Alan Karthikesalingam, Katherine A. Heller, Silvia Chiappa, Alexander D'Amour

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Why Fair Labels Can Yield Unfair Predictions: Graphical Conditions for Introduced Unfairness.

Carolyn Ashurst, Ryan Carey, Silvia Chiappa, Tom Everitt

Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021

Statistical discrimination in learning agents.

Edgar A. Duéñez-Guzmán, Kevin R. McKee, Yiran Mao, Ben Coppin, Silvia Chiappa, Alexander Sasha Vezhnevets, Michiel A. Bakker, Yoram Bachrach, Suzanne Sadedin, William Isaac, Karl Tuyls, Joel Z. Leibo

CoRR, 2021

Prequential MDL for Causal Structure Learning with Neural Networks.

Jörg Bornschein, Silvia Chiappa, Alan Malek, Nan Rosemary Ke

CoRR, 2021

Fairness with Continuous Optimal Transport.

Silvia Chiappa, Aldo Pacchiano

CoRR, 2021

Asymptotically Best Causal Effect Identification with Multi-Armed Bandits.

Alan Malek, Silvia Chiappa

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020

A General Approach to Fairness with Optimal Transport.

Silvia Chiappa, Ray Jiang, Tom Stepleton, Aldo Pacchiano, Heinrich Jiang, John Aslanides

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019

Unsupervised Separation of Dynamics from Pixels.

Silvia Chiappa, Ulrich Paquet

CoRR, 2019

Meta-learning of Sequential Strategies.

Pedro A. Ortega, Jane X. Wang, Mark Rowland, Tim Genewein, Zeb Kurth-Nelson, Razvan Pascanu, Nicolas Heess, Joel Veness, Alexander Pritzel, Pablo Sprechmann, Siddhant M. Jayakumar, Tom McGrath, Kevin J. Miller, Mohammad Gheshlaghi Azar, Ian Osband, Neil C. Rabinowitz, András György, Silvia Chiappa, Simon Osindero, Yee Whye Teh, Hado van Hasselt, Nando de Freitas, Matthew M. Botvinick, Shane Legg

CoRR, 2019

Causal Reasoning from Meta-reinforcement Learning.

Ishita Dasgupta, Jane X. Wang, Silvia Chiappa, Jovana Mitrovic, Pedro A. Ortega, David Raposo, Edward Hughes, Peter W. Battaglia, Matthew M. Botvinick, Zeb Kurth-Nelson

CoRR, 2019

Wasserstein Fair Classification.

Ray Jiang, Aldo Pacchiano, Tom Stepleton, Heinrich Jiang, Silvia Chiappa

Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, 2019

Fairness in Machine Learning.

Luca Oneto, Silvia Chiappa

Proceedings of the Recent Trends in Learning From Data, 2019

Degenerate Feedback Loops in Recommender Systems.

Ray Jiang, Silvia Chiappa, Tor Lattimore, András György, Pushmeet Kohli

Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, 2019

Path-Specific Counterfactual Fairness.

Silvia Chiappa

Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

2018

A Causal Bayesian Networks Viewpoint on Fairness.

Silvia Chiappa, William S. Isaac

Proceedings of the Privacy and Identity Management. Fairness, Accountability, and Transparency in the Age of Big Data, 2018

2017

Recurrent Environment Simulators.

Silvia Chiappa, Sébastien Racanière, Daan Wierstra, Shakir Mohamed

Proceedings of the 5th International Conference on Learning Representations, 2017

2014

Explicit-Duration Markov Switching Models.

Silvia Chiappa

Found. Trends Mach. Learn., 2014

2010

Movement extraction by detecting dynamics switches and repetitions.

Silvia Chiappa, Jan Peters

Proceedings of the Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Proceedings of a meeting held 6-9 December 2010, 2010

2009

A Bayesian Approach to Graphy Regression with Relevant Subgraph Selection.

Silvia Chiappa, Hiroto Saigo, Koji Tsuda

Proceedings of the SIAM International Conference on Data Mining, 2009

2008

Using Bayesian Dynamical Systems for Motion Template Libraries.

Silvia Chiappa, Jens Kober, Jan Peters

Proceedings of the Advances in Neural Information Processing Systems 21, 2008

A Bayesian Approach to Switching Linear Gaussian State-Space Models for Unsupervised Time-Series Segmentation.

Silvia Chiappa

Proceedings of the Seventh International Conference on Machine Learning and Applications, 2008

2007

Bayesian Factorial Linear Gaussian State-Space Models for Biosignal Decomposition.

Silvia Chiappa, David Barber

IEEE Signal Process. Lett., 2007

2006

EEG classification using generative independent component analysis.

Silvia Chiappa, David Barber

Neurocomputing, 2006

Unified Inference for Variational Bayesian Linear Gaussian State-Space Models.

David Barber, Silvia Chiappa

Proceedings of the Advances in Neural Information Processing Systems 19, 2006

2005

generative independent component analysis for EEG classification.

Silvia Chiappa, David Barber

Proceedings of the 13th European Symposium on Artificial Neural Networks, 2005

2004

HMM and IOHMM modeling of EEG rhythms for asynchronous BCI systems.

Silvia Chiappa, Nicolas Donckers, Samy Bengio, Frédéric Vrins

Proceedings of the 12th European Symposium on Artificial Neural Networks, 2004