HMM and IOHMM modeling of EEG rhythms for asynchronous BCI systems. - Silvia Chiappa; Nicolas Donckers; Samy Bengio; Frédéric Vrins (ESANN, 2004)

generative independent component analysis for EEG classification. - Silvia Chiappa; David Barber (ESANN, 2005)

Unified Inference for Variational Bayesian Linear Gaussian State-Space Models. - David Barber; Silvia Chiappa (NIPS, 2006)

EEG classification using generative independent component analysis. - Silvia Chiappa; David Barber (Neurocomputing, 2006)

Bayesian Factorial Linear Gaussian State-Space Models for Biosignal Decomposition. - Silvia Chiappa; David Barber (IEEE Signal Process. Lett., 2007)

A Bayesian Approach to Switching Linear Gaussian State-Space Models for Unsupervised Time-Series Segmentation. - Silvia Chiappa (ICMLA, 2008)

Using Bayesian Dynamical Systems for Motion Template Libraries. - Silvia Chiappa; Jens Kober; Jan Peters 001 (NIPS, 2008)

A Bayesian Approach to Graphy Regression with Relevant Subgraph Selection. - Silvia Chiappa; Hiroto Saigo; Koji Tsuda (SDM, 2009)

Movement extraction by detecting dynamics switches and repetitions. - Silvia Chiappa; Jan Peters 001 (NIPS, 2010)

Explicit-Duration Markov Switching Models. - Silvia Chiappa (Found. Trends Mach. Learn., 2014)

Recurrent Environment Simulators. - Silvia Chiappa; Sébastien Racanière; Daan Wierstra; Shakir Mohamed (ICLR (Poster), 2017)

A Causal Bayesian Networks Viewpoint on Fairness. - Silvia Chiappa; William S. Isaac (Privacy and Identity Management, 2018)

Path-Specific Counterfactual Fairness. - Silvia Chiappa (AAAI, 2019)

Degenerate Feedback Loops in Recommender Systems. - Ray Jiang; Silvia Chiappa; Tor Lattimore; András György 001; Pushmeet Kohli (AIES, 2019)

Fairness in Machine Learning. - Luca Oneto; Silvia Chiappa (INNSBDDL (Tutorials), 2019)

Wasserstein Fair Classification. - Ray Jiang; Aldo Pacchiano; Tom Stepleton; Heinrich Jiang; Silvia Chiappa (UAI, 2019)

Causal Reasoning from Meta-reinforcement Learning. - Ishita Dasgupta 001; Jane X. Wang; Silvia Chiappa; Jovana Mitrovic; Pedro A. Ortega; David Raposo; Edward Hughes 001; Peter W. Battaglia; Matthew M. Botvinick; Zeb Kurth-Nelson (CoRR, 2019)

Meta-learning of Sequential Strategies. - Pedro A. Ortega; Jane X. Wang; Mark Rowland 001; Tim Genewein; Zeb Kurth-Nelson; Razvan Pascanu; Nicolas Heess; Joel Veness; Alexander Pritzel; Pablo Sprechmann; Siddhant M. Jayakumar; Tom McGrath; et al. (CoRR, 2019)

Unsupervised Separation of Dynamics from Pixels. - Silvia Chiappa; Ulrich Paquet (CoRR, 2019)

A General Approach to Fairness with Optimal Transport. - Silvia Chiappa; Ray Jiang; Tom Stepleton; Aldo Pacchiano; Heinrich Jiang; John Aslanides (AAAI, 2020)

Asymptotically Best Causal Effect Identification with Multi-Armed Bandits. - Alan Malek; Silvia Chiappa (NeurIPS, 2021)

Fairness with Continuous Optimal Transport. - Silvia Chiappa; Aldo Pacchiano (CoRR, 2021)

Prequential MDL for Causal Structure Learning with Neural Networks. - Jörg Bornschein; Silvia Chiappa; Alan Malek; Nan Rosemary Ke (CoRR, 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 001; Karl Tuyls; Joel Z. Leibo (CoRR, 2021)

Why Fair Labels Can Yield Unfair Predictions: Graphical Conditions for Introduced Unfairness. - Carolyn Ashurst; Ryan Carey; Silvia Chiappa; Tom Everitt (AAAI, 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; et al. (NeurIPS, 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; et al. (CoRR, 2022)

Learning to Induce Causal Structure. - Nan Rosemary Ke; Silvia Chiappa; Jane Wang 001; Jörg Bornschein; Theophane Weber; Anirudh Goyal; Matthew M. Botvinick; Michael Mozer; Danilo Jimenez Rezende (CoRR, 2022)

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 (ICLR, 2023)

Constrained Causal Bayesian Optimization. - Virginia Aglietti; Alan Malek; Ira Ktena; Silvia Chiappa (ICML, 2023)

Additive Causal Bandits with Unknown Graph. - Alan Malek; Virginia Aglietti; Silvia Chiappa (ICML, 2023)

Transportability for Bandits with Data from Different Environments. - Alexis Bellot; Alan Malek; Silvia Chiappa (NeurIPS, 2023)

Functional causal Bayesian optimization. - Limor Gultchin; Virginia Aglietti; Alexis Bellot; Silvia Chiappa (UAI, 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; et al. (CoRR, 2023)

Pragmatic Fairness: Developing Policies with Outcome Disparity Control. - Limor Gultchin; Siyuan Guo; Alan Malek; Silvia Chiappa; Ricardo Silva 001 (CLeaR, 2024)

Towards Estimating Bounds on the Effect of Policies under Unobserved Confounding. - Alexis Bellot; Silvia Chiappa (NeurIPS, 2024)

Mind the Graph When Balancing Data for Fairness or Robustness. - Jessica Schrouff; Alexis Bellot; Amal Rannen-Triki; Alan Malek; Isabela Albuquerque; Arthur Gretton; Alexander D'Amour; Silvia Chiappa (NeurIPS, 2024)

FunBO: Discovering Acquisition Functions for Bayesian Optimization with FunSearch. - Virginia Aglietti; Ira Ktena; Jessica Schrouff; Eleni Sgouritsa; Francisco J. R. Ruiz; Alan Malek; Alexis Bellot; Silvia Chiappa (CoRR, 2024)

Prompting Strategies for Enabling Large Language Models to Infer Causation from Correlation. - Eleni Sgouritsa; Virginia Aglietti; Yee Whye Teh; Arnaud Doucet; Arthur Gretton; Silvia Chiappa (CoRR, 2024)

BIG-Bench Extra Hard. - Mehran Kazemi; Bahare Fatemi; Hritik Bansal; John Palowitch; Chrysovalantis Anastasiou; Sanket Vaibhav Mehta; Lalit K. Jain; Virginia Aglietti; Disha Jindal; Peter Chen; Nishanth Dikkala; Gladys Tyen; et al. (ACL (1), 2025)

BIG-Bench Extra Hard. - Mehran Kazemi; Bahare Fatemi; Hritik Bansal; John Palowitch; Chrysovalantis Anastasiou; Sanket Vaibhav Mehta; Kshitij Jain 001; Virginia Aglietti; Disha Jindal; Peter Chen; Nishanth Dikkala; Gladys Tyen; et al. (CoRR, 2025)