← Authors

Ricardo Silva

36 papers · 2006–2025 · 6 conferences · across top CS/AI conferences

Achievements

🧭 Keyword Pioneer 🐝 Cross-Pollinator (12) πŸƒ Academic Marathon (19) 🌈 Renaissance Researcher (5) πŸŒ‰ Interdisciplinary Bridge 🌍 Conference Polyglot (6) πŸ—ΊοΈ Taxonomy Completionist (18) 🐣 Hot Topic Early Bird 🌟 Keyword Trendsetter Combo (4) πŸ”¬ Deep Specialist (20) 🧬 Topic Evolution 🌱 Topic Pioneer πŸ† Keyword Champion πŸš€ Conference Pioneer πŸ—ƒοΈ Keyword Collector (134) ❓ The Questioner πŸ’Ž Century Club (36) πŸ“ˆ Trend Setter πŸ”₯ Unstoppable (10)

Conferences

NIPS (16) UAI (6) AISTATS (4) JMLR (4) CLEAR (3) ICML (3)

Top co-authors

Matt Kusner (5) Limor Gultchin (5) Matt J Kusner (5) David Watson (5) Yuchen Zhu (4) Niki Kilbertus (3) Nicolo Colombo (3) Chris Russell (3) Gecia Bravo-Hermsdorff (3) Joshua Loftus (3)

Keywords

causal inference (17) graphical model (6) causal discovery (5) counterfactual fairness (4) causal effect (4) bayesian inference (4) latent variable (4) structural causal model (3) instrumental variable (3) directed acyclic graph (3) gradient-based optimization (3) constrained optimization (3) algorithmic fairness (3) partial identification (2) treatment effect estimation (2) treatment effect (2) variational inference (2) gaussian process (2) observational study (2) structure learning (2)

Papers

BudgetIV: Optimal Partial Identification of Causal Effects with Mostly Invalid Instruments AISTATS 2025
AGM-TE: Approximate Generative Model Estimator of Transfer Entropy for Causal Discovery CLEAR 2025
Dual Risk Minimization: Towards Next-Level Robustness in Fine-tuning Zero-Shot Models NIPS 2024
Structured Learning of Compositional Sequential Interventions NIPS 2024
Pragmatic Fairness: Developing Policies with Outcome Disparity Control CLEAR 2024
Bounding causal effects with leaky instruments UAI 2024
Stochastic Causal Programming for Bounding Treatment Effects CLEAR 2023
Intervention Generalization: A View from Factor Graph Models NIPS 2023
Causal inference with treatment measurement error: a nonparametric instrumental variable approach UAI 2022
Causal discovery under a confounder blanket UAI 2022
When Do Flat Minima Optimizers Work? NIPS 2022
Causal Effect Inference for Structured Treatments NIPS 2021
Proximal Causal Learning with Kernels: Two-Stage Estimation and Moment Restriction ICML 2021
Operationalizing Complex Causes: A Pragmatic View of Mediation ICML 2021
Differentiable Causal Backdoor Discovery AISTATS 2020
A Class of Algorithms for General Instrumental Variable Models NIPS 2020
Learning Joint Nonlinear Effects from Single-variable Interventions in the Presence of Hidden Confounders UAI 2020
Neural Likelihoods via Cumulative Distribution Functions UAI 2020
Making Decisions that Reduce Discriminatory Impacts ICML 2019
The Sensitivity of Counterfactual Fairness to Unmeasured Confounding UAI 2019
Bayesian Semi-supervised Learning with Graph Gaussian Processes NIPS 2018
When Worlds Collide: Integrating Different Counterfactual Assumptions in Fairness NIPS 2017
Learning Instrumental Variables with Structural and Non-Gaussianity Assumptions JMLR 2017
Tomography of the London Underground: a Scalable Model for Origin-Destination Data NIPS 2017
Counterfactual Fairness NIPS 2017
Scaling Factorial Hidden Markov Models: Stochastic Variational Inference without Messages NIPS 2016
Observational-Interventional Priors for Dose-Response Learning NIPS 2016
Causal Inference through a Witness Protection Program JMLR 2016
Causal Inference through a Witness Protection Program NIPS 2014
Flexible sampling of discrete data correlations without the marginal distributions NIPS 2013
Discussion of β€œLearning Equivalence Classes of Acyclic Models with Latent and Selection Variables from Multiple Datasets with Overlapping Variables” AISTATS 2011
Mixed Cumulative Distribution Networks AISTATS 2011
Thinning Measurement Models and Questionnaire Design NIPS 2011
The Hidden Life of Latent Variables: Bayesian Learning with Mixed Graph Models JMLR 2009
Hidden Common Cause Relations in Relational Learning NIPS 2007
Learning the Structure of Linear Latent Variable Models JMLR 2006