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Graph Intervention Networks for Causal Effect Estimation


Jun 18, 2021
Jean Kaddour, Qi Liu, Yuchen Zhu, Matt J. Kusner, Ricardo Silva


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Operationalizing Complex Causes: A Pragmatic View of Mediation


Jun 10, 2021
Limor Gultchin, David S. Watson, Matt J. Kusner, Ricardo Silva

* International Conference on Machine Learning 2021 

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Operationalizing Complex Causes:A Pragmatic View of Mediation


Jun 09, 2021
Limor Gultchin, David S. Watson, Matt J. Kusner, Ricardo Silva

* ICML 2021 

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


Jun 06, 2021
Afsaneh Mastouri, Yuchen Zhu, Limor Gultchin, Anna Korba, Ricardo Silva, Matt J. Kusner, Arthur Gretton, Krikamol Muandet


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


Jun 16, 2020
Sorawit Saengkyongam, Ricardo Silva

* Accepted to The Conference on Uncertainty in Artificial Intelligence (UAI) 2020 

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A Class of Algorithms for General Instrumental Variable Models


Jun 11, 2020
Niki Kilbertus, Matt J. Kusner, Ricardo Silva

* Code at https://github.com/nikikilbertus/general-iv-models 

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Differentiable Causal Backdoor Discovery


Mar 03, 2020
Limor Gultchin, Matt J. Kusner, Varun Kanade, Ricardo Silva

* Published in the Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS) 2020, Palermo, Italy 

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


Feb 11, 2020
Alessio Pagani, Zhuangkun Wei, Ricardo Silva, Weisi Guo


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


Dec 09, 2019
Jacobo Roa-Vicens, Yuanbo Wang, Virgile Mison, Yarin Gal, Ricardo Silva

* Published as a workshop paper on NeurIPS 2019 Workshop on Robust AI in Financial Services. 33rd Conference on Neural Information Processing Systems (NeurIPS 2019), Vancouver, Canada 

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Counterfactual Distribution Regression for Structured Inference


Aug 20, 2019
Nicolo Colombo, Ricardo Silva, Soong M Kang, Arthur Gretton

* 24 pages, 5 figures 

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The Sensitivity of Counterfactual Fairness to Unmeasured Confounding


Jul 01, 2019
Niki Kilbertus, Philip J. Ball, Matt J. Kusner, Adrian Weller, Ricardo Silva

* published at UAI 2019 

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Towards Inverse Reinforcement Learning for Limit Order Book Dynamics


Jun 11, 2019
Jacobo Roa-Vicens, Cyrine Chtourou, Angelos Filos, Francisco Rullan, Yarin Gal, Ricardo Silva

* Published as a workshop paper on AI in Finance: Applications and Infrastructure for Multi-Agent Learning at the 36th International Conference on Machine Learning (ICML), Long Beach, California, PMLR97, 2019. Copyright 2019 by the author(s) 

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Neural Likelihoods via Cumulative Distribution Functions


Nov 02, 2018
Pawel Chilinski, Ricardo Silva

* 10 pages 

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Bayesian Semi-supervised Learning with Graph Gaussian Processes


Oct 12, 2018
Yin Cheng Ng, Nicolo Colombo, Ricardo Silva

* To appear in NIPS 2018 Fixed an error in Figure 2. The previous arxiv version contains two identical sub-figures 

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Causal Interventions for Fairness


Jun 06, 2018
Matt J. Kusner, Chris Russell, Joshua R. Loftus, Ricardo Silva


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Alpha-Beta Divergence For Variational Inference


May 20, 2018
Jean-Baptiste Regli, Ricardo Silva


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Causal Reasoning for Algorithmic Fairness


May 15, 2018
Joshua R. Loftus, Chris Russell, Matt J. Kusner, Ricardo Silva


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Counterfactual Fairness


Mar 08, 2018
Matt J. Kusner, Joshua R. Loftus, Chris Russell, Ricardo Silva


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A Dynamic Edge Exchangeable Model for Sparse Temporal Networks


Oct 11, 2017
Yin Cheng Ng, Ricardo Silva


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Scaling Factorial Hidden Markov Models: Stochastic Variational Inference without Messages


Oct 28, 2016
Yin Cheng Ng, Pawel Chilinski, Ricardo Silva

* Accepted to NIPS 2016 

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Observational-Interventional Priors for Dose-Response Learning


May 05, 2016
Ricardo Silva


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Bayesian Inference in Cumulative Distribution Fields


Nov 09, 2015
Ricardo Silva

* 14 pages, 4 figures. Presented at the 12th Brazilian Meeting on Bayesian Statistics 

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


Nov 09, 2015
Ricardo Silva, Shohei Shimizu

* 12 pages, 4 figures 

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Causal Inference through a Witness Protection Program


Oct 30, 2014
Ricardo Silva, Robin Evans

* 41 pages, 7 figures 

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Gaussian Process Structural Equation Models with Latent Variables


Aug 09, 2014
Ricardo Silva, Robert B. Gramacy

* Appears in Proceedings of the Twenty-Sixth Conference on Uncertainty in Artificial Intelligence (UAI2010) 

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Flexible sampling of discrete data correlations without the marginal distributions


Nov 14, 2013
Alfredo Kalaitzis, Ricardo Silva

* An overhauled version of the experimental section moved to the main paper. Old experimental section moved to supplementary material 

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Ranking relations using analogies in biological and information networks


Aug 29, 2013
Ricardo Silva, Katherine Heller, Zoubin Ghahramani, Edoardo M. Airoldi

* Annals of Applied Statistics 2010, Vol. 4, No. 2, 615-644 
* Published in at http://dx.doi.org/10.1214/09-AOAS321 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

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