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ORCIDSilvia Chiappa
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2020 – today
- 2024
[c23]Limor Gultchin, Siyuan Guo, Alan Malek, Silvia Chiappa, Ricardo Silva:
Pragmatic Fairness: Developing Policies with Outcome Disparity Control. CLeaR 2024: 243-264- [i22]Virginia Aglietti, Ira Ktena, Jessica Schrouff, Eleni Sgouritsa, Francisco J. R. Ruiz, Alan Malek, Alexis Bellot, Silvia Chiappa:
FunBO: Discovering Acquisition Functions for Bayesian Optimization with FunSearch. CoRR abs/2406.04824 (2024) - [i21]Jessica Schrouff, Alexis Bellot, Amal Rannen-Triki, Alan Malek, Isabela Albuquerque, Arthur Gretton, Alexander D'Amour, Silvia Chiappa:
Mind the Graph When Balancing Data for Fairness or Robustness. CoRR abs/2406.17433 (2024) - 2023
- [c22]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:
Learning to Induce Causal Structure. ICLR 2023 - [c21]Virginia Aglietti, Alan Malek, Ira Ktena, Silvia Chiappa:
Constrained Causal Bayesian Optimization. ICML 2023: 304-321 - [c20]Alan Malek, Virginia Aglietti, Silvia Chiappa:
Additive Causal Bandits with Unknown Graph. ICML 2023: 23574-23589 - [c19]Alexis Bellot, Alan Malek, Silvia Chiappa:
Transportability for Bandits with Data from Different Environments. NeurIPS 2023 - [c18]Limor Gultchin, Virginia Aglietti, Alexis Bellot, Silvia Chiappa:
Functional causal Bayesian optimization. UAI 2023: 756-765 - [i20]Limor Gultchin, Siyuan Guo, Alan Malek, Silvia Chiappa, Ricardo Silva:
Pragmatic Fairness: Developing Policies with Outcome Disparity Control. CoRR abs/2301.12278 (2023) - [i19]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:
DiscoGen: Learning to Discover Gene Regulatory Networks. CoRR abs/2304.05823 (2023) - [i18]Virginia Aglietti, Alan Malek, Ira Ktena, Silvia Chiappa:
Constrained Causal Bayesian Optimization. CoRR abs/2305.20011 (2023) - [i17]Limor Gultchin, Virginia Aglietti, Alexis Bellot, Silvia Chiappa:
Functional Causal Bayesian Optimization. CoRR abs/2306.06409 (2023) - [i16]Alan Malek, Virginia Aglietti, Silvia Chiappa:
Additive Causal Bandits with Unknown Graph. CoRR abs/2306.07858 (2023) - 2022
- [c17]Carolyn Ashurst, Ryan Carey, Silvia Chiappa, Tom Everitt:
Why Fair Labels Can Yield Unfair Predictions: Graphical Conditions for Introduced Unfairness. AAAI 2022: 9494-9503 - [c16]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:
Diagnosing failures of fairness transfer across distribution shift in real-world medical settings. NeurIPS 2022 - [i15]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:
Maintaining fairness across distribution shift: do we have viable solutions for real-world applications? CoRR abs/2202.01034 (2022) - [i14]Carolyn Ashurst, Ryan Carey, Silvia Chiappa, Tom Everitt:
Why Fair Labels Can Yield Unfair Predictions: Graphical Conditions for Introduced Unfairness. CoRR abs/2202.10816 (2022) - [i13]Nan Rosemary Ke, Silvia Chiappa, Jane Wang, Jörg Bornschein, Theophane Weber, Anirudh Goyal, Matthew M. Botvinick, Michael Mozer, Danilo Jimenez Rezende:
Learning to Induce Causal Structure. CoRR abs/2204.04875 (2022) - 2021
- [c15]Alan Malek, Silvia Chiappa:
Asymptotically Best Causal Effect Identification with Multi-Armed Bandits. NeurIPS 2021: 21960-21971 - [i12]Silvia Chiappa, Aldo Pacchiano:
Fairness with Continuous Optimal Transport. CoRR abs/2101.02084 (2021) - [i11]Jörg Bornschein, Silvia Chiappa, Alan Malek, Nan Rosemary Ke:
Prequential MDL for Causal Structure Learning with Neural Networks. CoRR abs/2107.05481 (2021) - [i10]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:
Statistical discrimination in learning agents. CoRR abs/2110.11404 (2021) - 2020
- [c14]Silvia Chiappa, Ray Jiang, Tom Stepleton, Aldo Pacchiano, Heinrich Jiang, John Aslanides:
A General Approach to Fairness with Optimal Transport. AAAI 2020: 3633-3640 - [e1]Silvia Chiappa, Roberto Calandra:
The 23rd International Conference on Artificial Intelligence and Statistics, AISTATS 2020, 26-28 August 2020, Online [Palermo, Sicily, Italy]. Proceedings of Machine Learning Research 108, PMLR 2020 [contents] - [i9]Luca Oneto, Silvia Chiappa:
Fairness in Machine Learning. CoRR abs/2012.15816 (2020)
2010 – 2019
- 2019
- [c13]Silvia Chiappa:
Path-Specific Counterfactual Fairness. AAAI 2019: 7801-7808 - [c12]Ray Jiang, Silvia Chiappa, Tor Lattimore, András György
, Pushmeet Kohli:
Degenerate Feedback Loops in Recommender Systems. AIES 2019: 383-390 - [c11]Luca Oneto, Silvia Chiappa:
Fairness in Machine Learning. INNSBDDL (Tutorials) 2019: 155-196 - [c10]Ray Jiang, Aldo Pacchiano, Tom Stepleton, Heinrich Jiang, Silvia Chiappa:
Wasserstein Fair Classification. UAI 2019: 862-872 - [i8]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:
Causal Reasoning from Meta-reinforcement Learning. CoRR abs/1901.08162 (2019) - [i7]Ray Jiang, Silvia Chiappa, Tor Lattimore, András György, Pushmeet Kohli:
Degenerate Feedback Loops in Recommender Systems. CoRR abs/1902.10730 (2019) - [i6]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:
Meta-learning of Sequential Strategies. CoRR abs/1905.03030 (2019) - [i5]Silvia Chiappa, William S. Isaac:
A Causal Bayesian Networks Viewpoint on Fairness. CoRR abs/1907.06430 (2019) - [i4]Ray Jiang, Aldo Pacchiano, Tom Stepleton, Heinrich Jiang, Silvia Chiappa:
Wasserstein Fair Classification. CoRR abs/1907.12059 (2019) - [i3]Silvia Chiappa, Ulrich Paquet:
Unsupervised Separation of Dynamics from Pixels. CoRR abs/1907.12906 (2019) - [i2]Silvia Chiappa:
Explicit-Duration Markov Switching Models. CoRR abs/1909.05800 (2019) - 2018
- [c9]Silvia Chiappa, William S. Isaac:
A Causal Bayesian Networks Viewpoint on Fairness. Privacy and Identity Management 2018: 3-20 - 2017
- [c8]Silvia Chiappa, Sébastien Racanière, Daan Wierstra, Shakir Mohamed:
Recurrent Environment Simulators. ICLR (Poster) 2017 - [i1]Silvia Chiappa, Sébastien Racanière, Daan Wierstra, Shakir Mohamed:
Recurrent Environment Simulators. CoRR abs/1704.02254 (2017) - 2014
- [j3]Silvia Chiappa:
Explicit-Duration Markov Switching Models. Found. Trends Mach. Learn. 7(6): 803-886 (2014) - 2010
- [c7]Silvia Chiappa, Jan Peters:
Movement extraction by detecting dynamics switches and repetitions. NIPS 2010: 388-396
2000 – 2009
- 2009
- [c6]Silvia Chiappa, Hiroto Saigo, Koji Tsuda:
A Bayesian Approach to Graphy Regression with Relevant Subgraph Selection. SDM 2009: 295-304 - 2008
- [c5]Silvia Chiappa:
A Bayesian Approach to Switching Linear Gaussian State-Space Models for Unsupervised Time-Series Segmentation. ICMLA 2008: 3-9 - [c4]Silvia Chiappa, Jens Kober, Jan Peters:
Using Bayesian Dynamical Systems for Motion Template Libraries. NIPS 2008: 297-304 - 2007
- [j2]Silvia Chiappa, David Barber:
Bayesian Factorial Linear Gaussian State-Space Models for Biosignal Decomposition. IEEE Signal Process. Lett. 14(4): 267-270 (2007) - 2006
- [j1]Silvia Chiappa, David Barber:
EEG classification using generative independent component analysis. Neurocomputing 69(7-9): 769-777 (2006) - [c3]David Barber, Silvia Chiappa:
Unified Inference for Variational Bayesian Linear Gaussian State-Space Models. NIPS 2006: 81-88 - 2005
- [c2]Silvia Chiappa, David Barber:
generative independent component analysis for EEG classification. ESANN 2005: 297-302 - 2004
- [c1]Silvia Chiappa, Nicolas Donckers, Samy Bengio, Frédéric Vrins:
HMM and IOHMM modeling of EEG rhythms for asynchronous BCI systems. ESANN 2004: 193-204
Coauthor Index
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last updated on 2024-10-04 20:03 CEST by the dblp team
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