Ricardo Silva

According to our database, Ricardo Silva authored at least 48 papers between 2012 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Fine-Tuning Pre-trained Language Models for Robust Causal Representation Learning.

Jialin Yu, Yuxiang Zhou, Yulan He, Nevin L. Zhang, Ricardo Silva

CoRR, 2024

Bounding Causal Effects with Leaky Instruments.

David S. Watson, Jordan Penn, Lee M. Gunderson, Gecia Bravo Hermsdorff, Afsaneh Mastouri, Ricardo Silva

CoRR, 2024

Counterfactual Fairness Is Not Demographic Parity, and Other Observations.

Ricardo Silva

CoRR, 2024

Structured Learning of Compositional Sequential Interventions.

Jialin Yu, Andreas Koukorinis, Nicolò Colombo, Yuchen Zhu, Ricardo Silva

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 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

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

Pragmatic Fairness: Developing Policies with Outcome Disparity Control.

Limor Gultchin, Siyuan Guo, Alan Malek, Silvia Chiappa, Ricardo Silva

Proceedings of the Causal Learning and Reasoning, 2024

2023

Spawrious: A Benchmark for Fine Control of Spurious Correlation Biases.

Aengus Lynch, Gbètondji J.-S. Dovonon, Jean Kaddour, Ricardo Silva

CoRR, 2023

Intervention Generalization: A View from Factor Graph Models.

Gecia Bravo Hermsdorff, David S. Watson, Jialin Yu, Jakob Zeitler, Ricardo Silva

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Stochastic Causal Programming for Bounding Treatment Effects.

Kirtan Padh, Jakob Zeitler, David S. Watson, Matt J. Kusner, Ricardo Silva, Niki Kilbertus

Proceedings of the Conference on Causal Learning and Reasoning, 2023

2022

Neural Network Approximation of Graph Fourier Transform for Sparse Sampling of Networked Dynamics.

Alessio Pagani, Zhuangkun Wei, Ricardo Silva, Weisi Guo

ACM Trans. Internet Techn., 2022

Causal Machine Learning: A Survey and Open Problems.

Jean Kaddour, Aengus Lynch, Qi Liu, Matt J. Kusner, Ricardo Silva

CoRR, 2022

The Causal Marginal Polytope for Bounding Treatment Effects.

Jakob Zeitler, Ricardo Silva

CoRR, 2022

Questions for Flat-Minima Optimization of Modern Neural Networks.

Jean Kaddour, Linqing Liu, Ricardo Silva, Matt J. Kusner

CoRR, 2022

Causal inference with treatment measurement error: a nonparametric instrumental variable approach.

Yuchen Zhu, Limor Gultchin, Arthur Gretton, Matt J. Kusner, Ricardo Silva

Proceedings of the Uncertainty in Artificial Intelligence, 2022

Causal discovery under a confounder blanket.

David S. Watson, Ricardo Silva

Proceedings of the Uncertainty in Artificial Intelligence, 2022

When Do Flat Minima Optimizers Work?

Jean Kaddour, Linqing Liu, Ricardo Silva, Matt J. Kusner

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Graph and tensor-train recurrent neural networks for high-dimensional models of limit order books.

Jacobo Roa-Vicens, Yao Lei Xu, Ricardo Silva, Danilo P. Mandic

Proceedings of the 3rd ACM International Conference on AI in Finance, 2022

2021

Graph Intervention Networks for Causal Effect Estimation.

Jean Kaddour, Qi Liu, Yuchen Zhu, Matt J. Kusner, Ricardo Silva

CoRR, 2021

Causal Effect Inference for Structured Treatments.

Jean Kaddour, Yuchen Zhu, Qi Liu, Matt J. Kusner, Ricardo Silva

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

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

Afsaneh Mastouri, Yuchen Zhu, Limor Gultchin, Anna Korba, Ricardo Silva, Matt J. Kusner, Arthur Gretton, Krikamol Muandet

Proceedings of the 38th International Conference on Machine Learning, 2021

Operationalizing Complex Causes: A Pragmatic View of Mediation.

Limor Gultchin, David S. Watson, Matt J. Kusner, Ricardo Silva

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

Neural Network Approximation of Graph Fourier Transforms for Sparse Sampling of Networked Flow Dynamics.

Alessio Pagani, Zhuangkun Wei, Ricardo Silva, Weisi Guo

CoRR, 2020

Neural Likelihoods via Cumulative Distribution Functions.

Pawel M. Chilinski, Ricardo Silva

Proceedings of the Thirty-Sixth Conference on Uncertainty in Artificial Intelligence, 2020

A Class of Algorithms for General Instrumental Variable Models.

Niki Kilbertus, Matt J. Kusner, Ricardo Silva

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Differentiable Causal Backdoor Discovery.

Limor Gultchin, Matt J. Kusner, Varun Kanade, Ricardo Silva

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

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, John D. Ainsworth, Huai Leng Pisaniello, Thomas A. House, Mark Lunt, Carolyn Gamble, Caroline Sanders, David M. Schultz, Jamie C. Sergeant, John McBeth

npj Digit. Medicine, 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

CoRR, 2019

Counterfactual Distribution Regression for Structured Inference.

Nicolò Colombo, Ricardo Silva, Soong Moon Kang, Arthur Gretton

CoRR, 2019

Towards Inverse Reinforcement Learning for Limit Order Book Dynamics.

Jacobo Roa-Vicens, Cyrine Chtourou, Angelos Filos, Francisco Rullan, Yarin Gal, Ricardo Silva

CoRR, 2019

The Sensitivity of Counterfactual Fairness to Unmeasured Confounding.

Niki Kilbertus, Philip J. Ball, Matt J. Kusner, Adrian Weller, Ricardo Silva

Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, 2019

Making Decisions that Reduce Discriminatory Impacts.

Matt J. Kusner, Chris Russell, Joshua R. Loftus, Ricardo Silva

Proceedings of the 36th International Conference on Machine Learning, 2019

2018

Bayesian Semi-supervised Learning with Graph Gaussian Processes.

Yin Cheng Ng, Ricardo Silva

CoRR, 2018

Causal Interventions for Fairness.

Matt J. Kusner, Chris Russell, Joshua R. Loftus, Ricardo Silva

CoRR, 2018

Causal Reasoning for Algorithmic Fairness.

Joshua R. Loftus, Chris Russell, Matt J. Kusner, Ricardo Silva

CoRR, 2018

Alpha-Beta Divergence For Variational Inference.

Jean-Baptiste Regli, Ricardo Silva

CoRR, 2018

Visualizing a Team's Goal Chances in Soccer from Attacking Events: A Bayesian Inference Approach.

Gavin A. Whitaker, Ricardo Silva, Daniel Edwards

Big Data, 2018

Bayesian Semi-supervised Learning with Graph Gaussian Processes.

Yin Cheng Ng, Nicolò Colombo, Ricardo Silva

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

2017

Causality.

Ricardo Silva

Proceedings of the Encyclopedia of Machine Learning and Data Mining, 2017

Learning Instrumental Variables with Structural and Non-Gaussianity Assumptions.

Ricardo Silva, Shohei Shimizu

J. Mach. Learn. Res., 2017

A Dynamic Edge Exchangeable Model for Sparse Temporal Networks.

Yin Cheng Ng, Ricardo Silva

CoRR, 2017

When Worlds Collide: Integrating Different Counterfactual Assumptions in Fairness.

Chris Russell, Matt J. Kusner, Joshua R. Loftus, Ricardo Silva

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Counterfactual Fairness.

Matt J. Kusner, Joshua R. Loftus, Chris Russell, Ricardo Silva

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

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

Nicolò Colombo, Ricardo Silva, Soong Moon Kang

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Visualization of Topic-Sentiment Dynamics in Crowdfunding Projects.

Rafael Augusto Ferreira do Carmo, Soong Moon Kang, Ricardo Silva

Proceedings of the Advances in Intelligent Data Analysis XVI, 2017

2016

Causal Inference through a Witness Protection Program.

Ricardo Silva, Robin J. Evans

J. Mach. Learn. Res., 2016

Observational-Interventional Priors for Dose-Response Learning.

Ricardo Silva

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Scaling Factorial Hidden Markov Models: Stochastic Variational Inference without Messages.

Yin Cheng Ng, Pawel M. Chilinski, Ricardo Silva

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

2012

Latent Composite Likelihood Learning for the Structured Canonical Correlation Model.

Ricardo Silva

Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence, 2012