Latent Composite Likelihood Learning for the Structured Canonical Correlation Model. - Ricardo Silva 001 (UAI, 2012)

Scaling Factorial Hidden Markov Models: Stochastic Variational Inference without Messages. - Yin Cheng Ng; Pawel M. Chilinski; Ricardo Silva 001 (NIPS, 2016)

Observational-Interventional Priors for Dose-Response Learning. - Ricardo Silva 001 (NIPS, 2016)

Causal Inference through a Witness Protection Program. - Ricardo Silva 001; Robin J. Evans 002 (J. Mach. Learn. Res., 2016)

Visualization of Topic-Sentiment Dynamics in Crowdfunding Projects. - Rafael Augusto Ferreira do Carmo; Soong Moon Kang; Ricardo Silva 001 (IDA, 2017)

Tomography of the London Underground: a Scalable Model for Origin-Destination Data. - Nicolò Colombo; Ricardo Silva 001; Soong Moon Kang (NIPS, 2017)

Counterfactual Fairness. - Matt J. Kusner; Joshua R. Loftus; Chris Russell 001; Ricardo Silva 001 (NIPS, 2017)

When Worlds Collide: Integrating Different Counterfactual Assumptions in Fairness. - Chris Russell 001; Matt J. Kusner; Joshua R. Loftus; Ricardo Silva 001 (NIPS, 2017)

A Dynamic Edge Exchangeable Model for Sparse Temporal Networks. - Yin Cheng Ng; Ricardo Silva 001 (CoRR, 2017)

Learning Instrumental Variables with Structural and Non-Gaussianity Assumptions. - Ricardo Silva 001; Shohei Shimizu (J. Mach. Learn. Res., 2017)

Causality. - Ricardo Silva 001 (Encyclopedia of Machine Learning and Data Mining, 2017)

Bayesian Semi-supervised Learning with Graph Gaussian Processes. - Yin Cheng Ng; Nicolò Colombo; Ricardo Silva 001 (NeurIPS, 2018)

Visualizing a Team's Goal Chances in Soccer from Attacking Events: A Bayesian Inference Approach. - Gavin A. Whitaker; Ricardo Silva 001; Daniel Edwards (Big Data, 2018)

Alpha-Beta Divergence For Variational Inference. - Jean-Baptiste Regli; Ricardo Silva 001 (CoRR, 2018)

Causal Reasoning for Algorithmic Fairness. - Joshua R. Loftus; Chris Russell 001; Matt J. Kusner; Ricardo Silva 001 (CoRR, 2018)

Causal Interventions for Fairness. - Matt J. Kusner; Chris Russell 001; Joshua R. Loftus; Ricardo Silva 001 (CoRR, 2018)

Bayesian Semi-supervised Learning with Graph Gaussian Processes. - Yin Cheng Ng; Ricardo Silva 001 (CoRR, 2018)

Making Decisions that Reduce Discriminatory Impacts. - Matt J. Kusner; Chris Russell 001; Joshua R. Loftus; Ricardo Silva 001 (ICML, 2019)

The Sensitivity of Counterfactual Fairness to Unmeasured Confounding. - Niki Kilbertus; Philip J. Ball; Matt J. Kusner; Adrian Weller; Ricardo Silva 001 (UAI, 2019)

Towards Inverse Reinforcement Learning for Limit Order Book Dynamics. - Jacobo Roa-Vicens; Cyrine Chtourou; Angelos Filos; Francisco Rullan; Yarin Gal; Ricardo Silva 001 (CoRR, 2019)

Counterfactual Distribution Regression for Structured Inference. - Nicolò Colombo; Ricardo Silva 001; Soong Moon Kang; Arthur Gretton (CoRR, 2019)

Adversarial recovery of agent rewards from latent spaces of the limit order book. - Jacobo Roa-Vicens; Yuanbo Wang; Virgile Mison; Yarin Gal; Ricardo Silva 001 (CoRR, 2019)

How the weather affects the pain of citizen scientists using a smartphone app. - William G. Dixon; Anna L. Beukenhorst; Belay Birlie Yimer; Louise Cook; Antonio Gasparrini; Tal El-Hay; Bruce Hellman; Ben James; Ana M. Vicedo-Cabrera; Malcolm Maclure; Ricardo Silva 001; John D. Ainsworth; et al. (npj Digit. Medicine, 2019)

Differentiable Causal Backdoor Discovery. - Limor Gultchin; Matt J. Kusner; Varun Kanade; Ricardo Silva 001 (AISTATS, 2020)

A Class of Algorithms for General Instrumental Variable Models. - Niki Kilbertus; Matt J. Kusner; Ricardo Silva 001 (NeurIPS, 2020)

Neural Likelihoods via Cumulative Distribution Functions. - Pawel M. Chilinski; Ricardo Silva 001 (UAI, 2020)

Neural Network Approximation of Graph Fourier Transforms for Sparse Sampling of Networked Flow Dynamics. - Alessio Pagani; Zhuangkun Wei; Ricardo Silva 001; Weisi Guo (CoRR, 2020)

Operationalizing Complex Causes: A Pragmatic View of Mediation. - Limor Gultchin; David S. Watson; Matt J. Kusner; Ricardo Silva 001 (ICML, 2021)

Proximal Causal Learning with Kernels: Two-Stage Estimation and Moment Restriction. - Afsaneh Mastouri; Yuchen Zhu; Limor Gultchin; Anna Korba; Ricardo Silva 001; Matt J. Kusner; Arthur Gretton; Krikamol Muandet (ICML, 2021)

Causal Effect Inference for Structured Treatments. - Jean Kaddour; Yuchen Zhu; Qi Liu 049; Matt J. Kusner; Ricardo Silva 001 (NeurIPS, 2021)

Graph Intervention Networks for Causal Effect Estimation. - Jean Kaddour; Qi Liu 049; Yuchen Zhu; Matt J. Kusner; Ricardo Silva 001 (CoRR, 2021)

Graph and tensor-train recurrent neural networks for high-dimensional models of limit order books. - Jacobo Roa-Vicens; Yao Lei Xu; Ricardo Silva 001; Danilo P. Mandic (ICAIF, 2022)

When Do Flat Minima Optimizers Work? - Jean Kaddour; Linqing Liu; Ricardo Silva 001; Matt J. Kusner (NeurIPS, 2022)

Causal discovery under a confounder blanket. - David S. Watson; Ricardo Silva 001 (UAI, 2022)

Causal inference with treatment measurement error: a nonparametric instrumental variable approach. - Yuchen Zhu; Limor Gultchin; Arthur Gretton; Matt J. Kusner; Ricardo Silva 001 (UAI, 2022)

Questions for Flat-Minima Optimization of Modern Neural Networks. - Jean Kaddour; Linqing Liu; Ricardo Silva 001; Matt J. Kusner (CoRR, 2022)

The Causal Marginal Polytope for Bounding Treatment Effects. - Jakob Zeitler; Ricardo Silva 001 (CoRR, 2022)

Causal Machine Learning: A Survey and Open Problems. - Jean Kaddour; Aengus Lynch; Qi Liu 049; Matt J. Kusner; Ricardo Silva 001 (CoRR, 2022)

Neural Network Approximation of Graph Fourier Transform for Sparse Sampling of Networked Dynamics. - Alessio Pagani; Zhuangkun Wei; Ricardo Silva 001; Weisi Guo (ACM Trans. Internet Techn., 2022)

Stochastic Causal Programming for Bounding Treatment Effects. - Kirtan Padh; Jakob Zeitler; David S. Watson; Matt J. Kusner; Ricardo Silva 001; Niki Kilbertus (CLeaR, 2023)

Intervention Generalization: A View from Factor Graph Models. - Gecia Bravo Hermsdorff; David S. Watson; Jialin Yu; Jakob Zeitler; Ricardo Silva 001 (NeurIPS, 2023)

Spawrious: A Benchmark for Fine Control of Spurious Correlation Biases. - Aengus Lynch; Gbètondji J.-S. Dovonon; Jean Kaddour; Ricardo Silva 001 (CoRR, 2023)

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

Structured Learning of Compositional Sequential Interventions. - Jialin Yu; Andreas Koukorinis; Nicolò Colombo; Yuchen Zhu; Ricardo Silva 001 (NeurIPS, 2024)

Bounding causal effects with leaky instruments. - David S. Watson; Jordan Penn; Lee M. Gunderson; Gecia Bravo Hermsdorff; Afsaneh Mastouri; Ricardo Silva 001 (UAI, 2024)

Counterfactual Fairness Is Not Demographic Parity, and Other Observations. - Ricardo Silva 001 (CoRR, 2024)

Fine-Tuning Pre-trained Language Models for Robust Causal Representation Learning. - Jialin Yu; Yuxiang Zhou; Yulan He 001; Nevin L. Zhang; Ricardo Silva 001 (CoRR, 2024)

BudgetIV: Optimal Partial Identification of Causal Effects with Mostly Invalid Instruments. - Jordan Penn; Lee M. Gunderson; Gecia Bravo Hermsdorff; Ricardo Silva 001; David S. Watson (AISTATS, 2025)