Ricardo Silva
36 papers · 2006–2025 · 6 conferences · across top CS/AI conferences
Achievements
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πΊοΈ Taxonomy Completionist (18) π§ Keyword Pioneer π Renaissance Researcher (5) π Interdisciplinary Bridge π£ Hot Topic Early Bird
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Conference Polyglot
(6)
πΊοΈ
Taxonomy Completionist
(18)
π£
Hot Topic Early Bird
π
Keyword Trendsetter Combo
(4)
π¬
Deep Specialist
(20)
π§¬
Topic Evolution
π±
Topic Pioneer
π
Keyword Champion
π
Conference Pioneer
ποΈ
Keyword Collector
(134)
β
The Questioner
π
Century Club
(36)
π
Trend Setter
π₯
Unstoppable
(10)
Conferences
NIPS (16)
UAI (6)
AISTATS (4)
JMLR (4)
CLEAR (3)
ICML (3)
Top co-authors
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 βLearning Equivalence Classes of Acyclic Models with Latent and Selection Variables from Multiple Datasets with Overlapping Variablesβ
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