Ricardo Silva

发表

The Hidden Life of Latent Variables: Bayesian Learning with Mixed Graph Models

Zoubin Ghahramani, Ricardo Bezerra de Andrade e Silva, Zoubin Ghahramani, 2009, J. Mach. Learn. Res..

Automatic discovery of latent variable models

R. Scheines, Ricardo Silva, 2005 .

Counterfactual Fairness

Matt J. Kusner, Ricardo Silva, Chris Russell, 2017, NIPS.

When Worlds Collide: Integrating Different Counterfactual Assumptions in Fairness

Matt J. Kusner, Chris Russell, Joshua R. Loftus, 2017, NIPS.

Bayesian Inference for Discrete Mixed Graph Models : Normit Networks , Observable Independencies and Infinite Mixtures

Ricardo Silva, 2006 .

Differentiable Causal Backdoor Discovery

Matt J. Kusner, Varun Kanade, Ricardo Silva, 2020, AISTATS.

Making Decisions that Reduce Discriminatory Impact

Matt J. Kusner, Joshua R. Loftus, Ricardo Silva, 2019 .

Learning Associations by Discrete Measurement Models

R. Scheines, Ricardo Silva, 2005 .

Belief propagation and locally Bayesian learning

Adam N. Sanborn, Ricardo Silva, 2009 .

Causal discovery under a confounder blanket

Ricardo Silva, David Watson, 2022, 2205.05715.

Learning Instrumental Variables with Non-Gaussianity Assumptions: Theoretical Limitations and Practical Algorithms

Shohei Shimizu, Ricardo Silva, 2015, 1511.02722.

A Class of Algorithms for General Instrumental Variable Models pdf

Matt J. Kusner, Niki Kilbertus, Ricardo Silva, 2020, NeurIPS.

Causal discovery under a confounder blanket

Ricardo Silva, David Watson, 2022, UAI.

Causal Inference with Treatment Measurement Error: A Nonparametric Instrumental Variable Approach

Matt J. Kusner, A. Gretton, Ricardo Silva, 2022, UAI.

Stochastic Causal Programming for Bounding Treatment Effects

Matt J. Kusner, Niki Kilbertus, Ricardo Silva, 2022, ArXiv.

Proximal Causal Learning with Kernels: Two-Stage Estimation and Moment Restriction

Matt J. Kusner, A. Gretton, Krikamol Muandet, 2021, ICML.

Sparse Bayesian dynamic network models, with genomics applications

Thomas E. Bartlett, I. Kosmidis, Ricardo Silva, 2018 .

Graph Intervention Networks for Causal Effect Estimation pdf

Qi Liu, Matt J. Kusner, Jean Kaddour, 2021, ArXiv.

The role of analogies in biological data : a study in the exploratory analysis of protein-protein interactions

K. Heller, Ricardo Silva, 2006 .

Ranking Relations using Analogies in Biological and Information Networks

Edoardo M. Airoldi, Katherine A. Heller, Zoubin Ghahramani, 2009, The annals of applied statistics.

Bayesian Semi-supervised Learning with Graph Gaussian Processes

Yin Cheng Ng, Ricardo Silva, Ricardo Silva, 2018, NeurIPS.

Causal Reasoning for Algorithmic Fairness pdf

Matt J. Kusner, Ricardo Silva, Chris Russell, 2018, ArXiv.

Two-way sparsity for time-varying networks with applications in genomics

Ioannis Kosmidis, Ricardo Silva, Thomas E. Bartlett, 2018, The Annals of Applied Statistics.

Small sets of interacting proteins suggest functional linkage mechanisms via Bayesian analogical reasoning

Edoardo M. Airoldi, Katherine A. Heller, Ricardo Bezerra de Andrade e Silva, 2011, Bioinform..

Proximal Causal Learning with Kernels: Two-Stage Estimation and Moment Restriction

Matt J. Kusner, A. Gretton, Krikamol Muandet, 2021, ICML.

Comments on “ Causal inference using invariant prediction : identification and confidence intervals ” by Peters , Bühlmann and Meinshausen

Ricardo Silva, 2016 .

A MCMC Approach for Learning the Structure of Gaussian Acyclic Directed Mixed Graphs

Ricardo Bezerra de Andrade e Silva, Ricardo Silva, 2013, Statistical Models for Data Analysis.

On estimating covariances between many assets with histories of highly variable length

R. Gramacy, Ricardo Silva, Joo Hee Lee, 2007, 0710.5837.

Constraining bridges between levels of analysis: A computational justification for locally Bayesian learning

Ricardo Silva, Adam N. Sanborn, Ricardo Silva, 2013 .

Causal Machine Learning: A Survey and Open Problems

Matt J. Kusner, Ricardo Silva, Qi Liu, 2022, ArXiv.

Causal Inference through a Witness Protection Program

Ricardo Silva, Robin J. Evans, Ricardo Silva, 2014, J. Mach. Learn. Res..

Hidden Common Cause Relations in Relational Learning

Wei Chu, Zoubin Ghahramani, Ricardo Bezerra de Andrade e Silva, 2007, NIPS.

Learning Instrumental Variables with Structural and Non-Gaussianity Assumptions

Ricardo Silva, Shohei Shimizu, Shohei Shimizu, 2017, J. Mach. Learn. Res..

Learning the Structure of Linear Latent Variable Models

Richard Scheines, Clark Glymour, Ricardo Bezerra de Andrade e Silva, 2006, J. Mach. Learn. Res..

Making Decisions that Reduce Discriminatory Impacts

Matt J. Kusner, Ricardo Silva, Chris Russell, 2019, ICML.

Causal Effect Inference for Structured Treatments

Matt J. Kusner, Jean Kaddour, Yuchen Zhu, 2021, NeurIPS.

Operationalizing Complex Causes: A Pragmatic View of Mediation pdf

Matt J. Kusner, Limor Gultchin, Ricardo Silva, 2021, ICML.

Learning Measurement Models for Unobserved Variables

Richard Scheines, Clark Glymour, Ricardo Bezerra de Andrade e Silva, 2002, UAI.

The Sensitivity of Counterfactual Fairness to Unmeasured Confounding

Matt J. Kusner, Adrian Weller, Niki Kilbertus, 2019, UAI.

Observational-Interventional Priors for Dose-Response Learning

Ricardo Silva, Ricardo Silva, 2016, NIPS.

Intervention Generalization: A View from Factor Graph Models

Ricardo Silva, David Watson, Jialin Yu, 2023, NeurIPS.

Mixed Cumulative Distribution Networks

Yee Whye Teh, Ricardo Bezerra de Andrade e Silva, Charles Blundell, 2010, AISTATS.

Neural Likelihoods via Cumulative Distribution Functions pdf

Ricardo Silva, Pawel M. Chilinski, Ricardo Silva, 2018, UAI.

Adversarial recovery of agent rewards from latent spaces of the limit order book pdf

Yuanbo Wang, Ricardo Silva, Yarin Gal, 2019, ArXiv.

Stochastic Causal Programming for Bounding Treatment Effects

Matt J. Kusner, Niki Kilbertus, Ricardo Silva, 2022, CLeaR.

Causal Interventions for Fairness pdf

Matt J. Kusner, Ricardo Silva, Chris Russell, 2018, ArXiv.

A Dynamic Edge Exchangeable Model for Sparse Temporal Networks pdf

Ricardo Silva, Yin Cheng Ng, Ricardo Silva, 2017, ArXiv.

Research Statement

Ricardo Silva, 2006 .

Analogical Reasoning with Relational Bayesian Sets

Katherine A. Heller, Zoubin Ghahramani, Ricardo Bezerra de Andrade e Silva, 2007, AISTATS.

Small sets of interacting proteins suggest latent linkage mechanisms through analogical reasoning

Ricardo Silva, A Airoldi, Katherine A. Heller, 2007 .

Bayesian inference via projections

Ricardo Bezerra de Andrade e Silva, Alfredo A. Kalaitzis, Ricardo Silva, 2015, Stat. Comput..

Bayesian learning of measurement and structural models

Richard Scheines, Ricardo Bezerra de Andrade e Silva, R. Scheines, 2006, ICML.

Measuring Latent Causal Structure pdf

Ricardo Bezerra de Andrade e Silva, Ricardo Silva, 2010, ArXiv.

Thinning Measurement Models and Questionnaire Design

Ricardo Bezerra de Andrade e Silva, Ricardo Silva, 2011, NIPS.

Tomography of the London Underground: a Scalable Model for Origin-Destination Data

Nicolò Colombo, Ricardo Silva, Soong Moon Kang, 2017, NIPS.

Generalized measurement models

Richard Scheines, Ricardo Silva, R. Scheines, 2005 .

Gaussian Process Structural Equation Models with Latent Variables

Ricardo Silva, Robert B. Gramacy, R. Gramacy, 2010, UAI.

Learning Joint Nonlinear Effects from Single-variable Interventions in the Presence of Hidden Confounders

Sorawit Saengkyongam, Ricardo Silva, 2020, UAI.

The Causal Marginal Polytope for Bounding Treatment Effects

Ricardo Silva, Jakob Zeitler, 2022, ArXiv.

Counterfactual Distribution Regression for Structured Inference pdf

Arthur Gretton, Soong Moon Kang, Ricardo Silva, 2019, ArXiv.

Discussion of "Learning Equivalence Classes of Acyclic Models with Latent and Selection Variables from Multiple Datasets with Overlapping Variables"

Ricardo Bezerra de Andrade e Silva, Jiji Zhang, Jiji Zhang, 2011, AISTATS.

New d-separation identification results for learning continuous latent variable models

Richard Scheines, Ricardo Bezerra de Andrade e Silva, R. Scheines, 2005, ICML.