Silva, Ricardo

48 publications

CLeaR 2025 AGM-TE: Approximate Generative Model Estimator of Transfer Entropy for Causal Discovery Daniel Kornai, Ricardo Silva, Nikolaos Nikolaou

AISTATS 2025 BudgetIV: Optimal Partial Identification of Causal Effects with Mostly Invalid Instruments Jordan Penn, Lee M. Gunderson, Gecia Bravo-Hermsdorff, Ricardo Silva, David Watson

ICLRW 2025 Spawrious: A Benchmark for Fine Control of Spurious Correlation Biases Aengus Lynch, Gbetondji Jean-Sebastien Dovonon, Jean Kaddour, Ricardo Silva

UAI 2024 Bounding Causal Effects with Leaky Instruments David Watson, Jordan Penn, Lee Gunderson, Gecia Bravo-Hermsdorff, Afsaneh Mastouri, Ricardo Silva

ICMLW 2024 Dual Risk Minimization for Robust Fine-Tuning of Zero-Shot Models Kaican Li, Weiyan Xie, Ricardo Silva, Nevin L. Zhang

NeurIPS 2024 Dual Risk Minimization: Towards Next-Level Robustness in Fine-Tuning Zero-Shot Models Kaican Li, Weiyan Xie, Yongxiang Huang, Didan Deng, Lanqing Hong, Zhenguo Li, Ricardo Silva, Nevin L. Zhang

CLeaR 2024 Pragmatic Fairness: Developing Policies with Outcome Disparity Control Limor Gultchin, Siyuan Guo, Alan Malek, Silvia Chiappa, Ricardo Silva

NeurIPS 2024 Structured Learning of Compositional Sequential Interventions Jialin Yu, Andreas Koukorinis, Nicolò Colombo, Yuchen Zhu, Ricardo Silva

NeurIPS 2023 Intervention Generalization: A View from Factor Graph Models Gecia Bravo-Hermsdorff, David Watson, Jialin Yu, Jakob Zeitler, Ricardo Silva

CLeaR 2023 Stochastic Causal Programming for Bounding Treatment Effects Kirtan Padh, Jakob Zeitler, David Watson, Matt Kusner, Ricardo Silva, Niki Kilbertus

UAI 2022 Causal Discovery Under a Confounder Blanket David S. Watson, Ricardo Silva

UAI 2022 Causal Inference with Treatment Measurement Error: A Nonparametric Instrumental Variable Approach Yuchen Zhu, Limor Gultchin, Arthur Gretton, Matt J. Kusner, Ricardo Silva

NeurIPSW 2022 Evaluating the Impact of Geometric and Statistical Skews on Out-of-Distribution Generalization Performance Aengus Lynch, Jean Kaddour, Ricardo Silva

NeurIPSW 2022 Evaluating the Impact of Geometric and Statistical Skews on Out-of-Distribution Generalization Performance Aengus Lynch, Jean Kaddour, Ricardo Silva

NeurIPSW 2022 Partial Identification Without Distributional Assumptions Kirtan Padh, Jakob Zeitler, David Watson, Matt Kusner, Ricardo Silva, Niki Kilbertus

NeurIPS 2022 When Do Flat Minima Optimizers Work? Jean Kaddour, Linqing Liu, Ricardo Silva, Matt J Kusner

NeurIPS 2021 Causal Effect Inference for Structured Treatments Jean Kaddour, Yuchen Zhu, Qi Liu, Matt J Kusner, Ricardo Silva

ICML 2021 Operationalizing Complex Causes: A Pragmatic View of Mediation Limor Gultchin, David Watson, Matt Kusner, Ricardo Silva

ICML 2021 Proximal Causal Learning with Kernels: Two-Stage Estimation and Moment Restriction Afsaneh Mastouri, Yuchen Zhu, Limor Gultchin, Anna Korba, Ricardo Silva, Matt Kusner, Arthur Gretton, Krikamol Muandet

NeurIPS 2020 A Class of Algorithms for General Instrumental Variable Models Niki Kilbertus, Matt J Kusner, Ricardo Silva

AISTATS 2020 Differentiable Causal Backdoor Discovery Limor Gultchin, Matt Kusner, Varun Kanade, Ricardo Silva

UAI 2020 Learning Joint Nonlinear Effects from Single-Variable Interventions in the Presence of Hidden Confounders Sorawit Saengkyongam, Ricardo Silva

UAI 2020 Neural Likelihoods via Cumulative Distribution Functions Pawel Chilinski, Ricardo Silva

ICML 2019 Making Decisions That Reduce Discriminatory Impacts Matt Kusner, Chris Russell, Joshua Loftus, Ricardo Silva

UAI 2019 The Sensitivity of Counterfactual Fairness to Unmeasured Confounding Niki Kilbertus, Philip J. Ball, Matt J. Kusner, Adrian Weller, Ricardo Silva

NeurIPS 2018 Bayesian Semi-Supervised Learning with Graph Gaussian Processes Yin Cheng Ng, Nicolò Colombo, Ricardo Silva

UAI 2018 Proceedings of the Thirty-Fourth Conference on Uncertainty in Artificial Intelligence, UAI 2018, Monterey, California, USA, August 6-10, 2018 Amir Globerson, Ricardo Silva

NeurIPS 2017 Counterfactual Fairness Matt J Kusner, Joshua Loftus, Chris Russell, Ricardo Silva

JMLR 2017 Learning Instrumental Variables with Structural and Non-Gaussianity Assumptions Ricardo Silva, Shohei Shimizu

NeurIPS 2017 Tomography of the London Underground: A Scalable Model for Origin-Destination Data Nicolò Colombo, Ricardo Silva, Soong Moon Kang

NeurIPS 2017 When Worlds Collide: Integrating Different Counterfactual Assumptions in Fairness Chris Russell, Matt J Kusner, Joshua Loftus, Ricardo Silva

JMLR 2016 Causal Inference Through a Witness Protection Program Ricardo Silva, Robin Evans

NeurIPS 2016 Observational-Interventional Priors for Dose-Response Learning Ricardo Silva

UAI 2016 Proceedings of the UAI 2016 Workshop on Causation: Foundation to Application Co-Located with the 32nd Conference on Uncertainty in Artificial Intelligence (UAI 2016), Jersey City, USA, June 29, 2016 Frederick Eberhardt, Elias Bareinboim, Marloes H. Maathuis, Joris M. Mooij, Ricardo Silva

NeurIPS 2016 Scaling Factorial Hidden Markov Models: Stochastic Variational Inference Without Messages Yin Cheng Ng, Pawel M Chilinski, Ricardo Silva

UAI 2015 Proceedings of the UAI 2015 Workshop on Advances in Causal Inference Co-Located with the 31st Conference on Uncertainty in Artificial Intelligence (UAI 2015), Amsterdam, the Netherlands, July 16, 2015 Ricardo Silva, Ilya Shpitser, Robin J. Evans, Jonas Peters, Tom Claassen

NeurIPS 2014 Causal Inference Through a Witness Protection Program Ricardo Silva, Robin Evans

NeurIPS 2013 Flexible Sampling of Discrete Data Correlations Without the Marginal Distributions Alfredo Kalaitzis, Ricardo Silva

UAI 2012 Latent Composite Likelihood Learning for the Structured Canonical Correlation Model Ricardo Silva

AISTATS 2011 Discussion of “Learning Equivalence Classes of Acyclic Models with Latent and Selection Variables from Multiple Datasets with Overlapping Variables” Jiji Zhang, Ricardo Silva

AISTATS 2011 Mixed Cumulative Distribution Networks Ricardo Silva, Charles Blundell, Yee Whye Teh

NeurIPS 2011 Thinning Measurement Models and Questionnaire Design Ricardo Silva

AISTATS 2009 Factorial Mixture of Gaussians and the Marginal Independence Model Ricardo Silva, Zoubin Ghahramani

AISTATS 2009 MCMC Methods for Bayesian Mixtures of Copulas Ricardo Silva, Robert Gramacy

JMLR 2009 The Hidden Life of Latent Variables: Bayesian Learning with Mixed Graph Models Ricardo Silva, Zoubin Ghahramani

AISTATS 2007 Analogical Reasoning with Relational Bayesian Sets Ricardo Silva, Katherine A. Heller, Zoubin Ghahramani

NeurIPS 2007 Hidden Common Cause Relations in Relational Learning Ricardo Silva, Wei Chu, Zoubin Ghahramani

JMLR 2006 Learning the Structure of Linear Latent Variable Models Ricardo Silva, Richard Scheine, Clark Glymour, Peter Spirtes