Ricardo Silva - RankMe

71 papers A* 10A 10B 2Journal 37Unranked 9

Year	Rank	Type	Title / Venue / Authors
2025	A	`conf`	BudgetIV: Optimal Partial Identification of Causal Effects with Mostly Invalid Instruments. AISTATS Jordan Penn, Lee M. Gunderson, Gecia Bravo Hermsdorff, Ricardo Silva, David S. Watson
2025	B	`conf`	Causal Analysis of Intellectual Complexity in Software Engineering. IJCNN Chunhao Li, Mengdi Zhang, Sijia Meng, Xuning Gao, Jialin Yu, Jianyuan Hu, Shuo Wang, Ricardo Silva, Bin Chen
2025	J	`jnl`	Causal Machine Learning: A Survey and Open Problems. Found. Trends Optim. Jean Kaddour, Aengus Lynch, Qi Liu, Matt J. Kusner, Ricardo Silva
2024	J	`jnl`	Bounding Causal Effects with Leaky Instruments. CoRR David S. Watson, Jordan Penn, Lee M. Gunderson, Gecia Bravo Hermsdorff, Afsaneh Mastouri, Ricardo Silva
2024	A	`conf`	Bounding causal effects with leaky instruments. UAI David S. Watson, Jordan Penn, Lee M. Gunderson, Gecia Bravo Hermsdorff, Afsaneh Mastouri, Ricardo Silva
2024	J	`jnl`	Counterfactual Fairness Is Not Demographic Parity, and Other Observations. CoRR Ricardo Silva
2024	A*	`conf`	Dual Risk Minimization: Towards Next-Level Robustness in Fine-tuning Zero-Shot Models. NeurIPS Kaican Li, Weiyan Xie, Yongxiang Huang, Didan Deng, Lanqing Hong, Zhenguo Li, Ricardo Silva, Nevin L. Zhang
2024	J	`jnl`	Dual Risk Minimization: Towards Next-Level Robustness in Fine-tuning Zero-Shot Models. CoRR Kaican Li, Weiyan Xie, Yongxiang Huang, Didan Deng, Lanqing Hong, Zhenguo Li, Ricardo Silva, Nevin L. Zhang
2024	J	`jnl`	Fine-Tuning Pre-trained Language Models for Robust Causal Representation Learning. CoRR Jialin Yu, Yuxiang Zhou, Yulan He, Nevin L. Zhang, Ricardo Silva
2024	—	`conf`	Pragmatic Fairness: Developing Policies with Outcome Disparity Control. CLeaR Limor Gultchin, Siyuan Guo, Alan Malek, Silvia Chiappa, Ricardo Silva
2024	A*	`conf`	Structured Learning of Compositional Sequential Interventions. NeurIPS Jialin Yu, Andreas Koukorinis, Nicolò Colombo, Yuchen Zhu, Ricardo Silva
2024	J	`jnl`	Structured Learning of Compositional Sequential Interventions. CoRR Jialin Yu, Andreas Koukorinis, Nicolò Colombo, Yuchen Zhu, Ricardo Silva
2023	A*	`conf`	Intervention Generalization: A View from Factor Graph Models. NeurIPS Gecia Bravo Hermsdorff, David S. Watson, Jialin Yu, Jakob Zeitler, Ricardo Silva
2023	J	`jnl`	Intervention Generalization: A View from Factor Graph Models. CoRR Gecia Bravo Hermsdorff, David S. Watson, Jialin Yu, Jakob Zeitler, Ricardo Silva
2023	J	`jnl`	Pragmatic Fairness: Developing Policies with Outcome Disparity Control. CoRR Limor Gultchin, Siyuan Guo, Alan Malek, Silvia Chiappa, Ricardo Silva
2023	J	`jnl`	Spawrious: A Benchmark for Fine Control of Spurious Correlation Biases. CoRR Aengus Lynch, Gbètondji J.-S. Dovonon, Jean Kaddour, Ricardo Silva
2023	—	`conf`	Stochastic Causal Programming for Bounding Treatment Effects. CLeaR Kirtan Padh, Jakob Zeitler, David S. Watson, Matt J. Kusner, Ricardo Silva, Niki Kilbertus
2022	J	`jnl`	Causal Inference with Treatment Measurement Error: A Nonparametric Instrumental Variable Approach. CoRR Yuchen Zhu, Limor Gultchin, Arthur Gretton, Matt J. Kusner, Ricardo Silva
2022	J	`jnl`	Causal Machine Learning: A Survey and Open Problems. CoRR Jean Kaddour, Aengus Lynch, Qi Liu, Matt J. Kusner, Ricardo Silva
2022	A	`conf`	Causal discovery under a confounder blanket. UAI David S. Watson, Ricardo Silva
2022	J	`jnl`	Causal discovery under a confounder blanket. CoRR David S. Watson, Ricardo Silva
2022	A	`conf`	Causal inference with treatment measurement error: a nonparametric instrumental variable approach. UAI Yuchen Zhu, Limor Gultchin, Arthur Gretton, Matt J. Kusner, Ricardo Silva
2022	—	`conf`	Graph and tensor-train recurrent neural networks for high-dimensional models of limit order books. ICAIF Jacobo Roa-Vicens, Yao Lei Xu, Ricardo Silva, Danilo P. Mandic
2022	J	`jnl`	Neural Network Approximation of Graph Fourier Transform for Sparse Sampling of Networked Dynamics. ACM Trans. Internet Techn. Alessio Pagani, Zhuangkun Wei, Ricardo Silva, Weisi Guo
2022	J	`jnl`	Questions for Flat-Minima Optimization of Modern Neural Networks. CoRR Jean Kaddour, Linqing Liu, Ricardo Silva, Matt J. Kusner
2022	J	`jnl`	Stochastic Causal Programming for Bounding Treatment Effects. CoRR Kirtan Padh, Jakob Zeitler, David S. Watson, Matt J. Kusner, Ricardo Silva, Niki Kilbertus
2022	J	`jnl`	The Causal Marginal Polytope for Bounding Treatment Effects. CoRR Jakob Zeitler, Ricardo Silva
2022	A*	`conf`	When Do Flat Minima Optimizers Work? NeurIPS Jean Kaddour, Linqing Liu, Ricardo Silva, Matt J. Kusner
2021	A*	`conf`	Causal Effect Inference for Structured Treatments. NeurIPS Jean Kaddour, Yuchen Zhu, Qi Liu, Matt J. Kusner, Ricardo Silva
2021	J	`jnl`	Graph Intervention Networks for Causal Effect Estimation. CoRR Jean Kaddour, Qi Liu, Yuchen Zhu, Matt J. Kusner, Ricardo Silva
2021	A*	`conf`	Operationalizing Complex Causes: A Pragmatic View of Mediation. ICML Limor Gultchin, David S. Watson, Matt J. Kusner, Ricardo Silva
2021	J	`jnl`	Operationalizing Complex Causes: A Pragmatic View of Mediation. CoRR Limor Gultchin, David S. Watson, Matt J. Kusner, Ricardo Silva
2021	A*	`conf`	Proximal Causal Learning with Kernels: Two-Stage Estimation and Moment Restriction. ICML Afsaneh Mastouri, Yuchen Zhu, Limor Gultchin, Anna Korba, Ricardo Silva, Matt J. Kusner, Arthur Gretton, Krikamol Muandet
2021	J	`jnl`	Proximal Causal Learning with Kernels: Two-Stage Estimation and Moment Restriction. CoRR Afsaneh Mastouri, Yuchen Zhu, Limor Gultchin, Anna Korba, Ricardo Silva, Matt J. Kusner, Arthur Gretton, Krikamol Muandet
2020	A*	`conf`	A Class of Algorithms for General Instrumental Variable Models. NeurIPS Niki Kilbertus, Matt J. Kusner, Ricardo Silva
2020	J	`jnl`	A Class of Algorithms for General Instrumental Variable Models. CoRR Niki Kilbertus, Matt J. Kusner, Ricardo Silva
2020	A	`conf`	Differentiable Causal Backdoor Discovery. AISTATS Limor Gultchin, Matt J. Kusner, Varun Kanade, Ricardo Silva
2020	J	`jnl`	Differentiable Causal Backdoor Discovery. CoRR Limor Gultchin, Matt J. Kusner, Varun Kanade, Ricardo Silva
2020	A	`conf`	Neural Likelihoods via Cumulative Distribution Functions. UAI Pawel M. Chilinski, Ricardo Silva
2020	J	`jnl`	Neural Network Approximation of Graph Fourier Transforms for Sparse Sampling of Networked Flow Dynamics. CoRR Alessio Pagani, Zhuangkun Wei, Ricardo Silva, Weisi Guo
2019	J	`jnl`	Adversarial recovery of agent rewards from latent spaces of the limit order book. CoRR Jacobo Roa-Vicens, Yuanbo Wang, Virgile Mison, Yarin Gal, Ricardo Silva
2019	J	`jnl`	Counterfactual Distribution Regression for Structured Inference. CoRR Nicolò Colombo, Ricardo Silva, Soong Moon Kang, Arthur Gretton
2019	J	`jnl`	How the weather affects the pain of citizen scientists using a smartphone app. npj Digit. Medicine 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
2019	A*	`conf`	Making Decisions that Reduce Discriminatory Impacts. ICML Matt J. Kusner, Chris Russell, Joshua R. Loftus, Ricardo Silva
2019	A	`ed.`	Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, UAI 2019, Tel Aviv, Israel, July 22-25, 2019 UAI Amir Globerson, Ricardo Silva
2019	A	`conf`	The Sensitivity of Counterfactual Fairness to Unmeasured Confounding. UAI Niki Kilbertus, Philip J. Ball, Matt J. Kusner, Adrian Weller, Ricardo Silva
2019	J	`jnl`	The Sensitivity of Counterfactual Fairness to Unmeasured Confounding. CoRR Niki Kilbertus, Philip J. Ball, Matt J. Kusner, Adrian Weller, Ricardo Silva
2019	J	`jnl`	Towards Inverse Reinforcement Learning for Limit Order Book Dynamics. CoRR Jacobo Roa-Vicens, Cyrine Chtourou, Angelos Filos, Francisco Rullan, Yarin Gal, Ricardo Silva
2018	J	`jnl`	Alpha-Beta Divergence For Variational Inference. CoRR Jean-Baptiste Regli, Ricardo Silva
2018	A*	`conf`	Bayesian Semi-supervised Learning with Graph Gaussian Processes. NeurIPS Yin Cheng Ng, Nicolò Colombo, Ricardo Silva
2018	J	`jnl`	Bayesian Semi-supervised Learning with Graph Gaussian Processes. CoRR Yin Cheng Ng, Ricardo Silva
2018	J	`jnl`	Causal Interventions for Fairness. CoRR Matt J. Kusner, Chris Russell, Joshua R. Loftus, Ricardo Silva
2018	J	`jnl`	Causal Reasoning for Algorithmic Fairness. CoRR Joshua R. Loftus, Chris Russell, Matt J. Kusner, Ricardo Silva
2018	J	`jnl`	Neural Likelihoods via Cumulative Distribution Functions. CoRR Pawel M. Chilinski, Ricardo Silva
2018	A	`ed.`	Proceedings of the Thirty-Fourth Conference on Uncertainty in Artificial Intelligence, UAI 2018, Monterey, California, USA, August 6-10, 2018 UAI Amir Globerson, Ricardo Silva
2018	J	`jnl`	Visualizing a Team's Goal Chances in Soccer from Attacking Events: A Bayesian Inference Approach. Big Data Gavin A. Whitaker, Ricardo Silva, Daniel Edwards
2017	J	`jnl`	A Dynamic Edge Exchangeable Model for Sparse Temporal Networks. CoRR Yin Cheng Ng, Ricardo Silva
2017	—	`ch.`	Causality. Encyclopedia of Machine Learning and Data Mining Ricardo Silva
2017	—	`conf`	Counterfactual Fairness. NIPS Matt J. Kusner, Joshua R. Loftus, Chris Russell, Ricardo Silva
2017	J	`jnl`	Counterfactual Fairness. CoRR Matt J. Kusner, Joshua R. Loftus, Chris Russell, Ricardo Silva
2017	J	`jnl`	Learning Instrumental Variables with Structural and Non-Gaussianity Assumptions. J. Mach. Learn. Res. Ricardo Silva, Shohei Shimizu
2017	—	`ed.`	Proceedings of the UAI 2016 Workshop on Causation: Foundation to Application co-located with the 32nd Conference on Uncertainty in Artificial Intelligence (UAI 2016), Jersey City, USA, June 29, 2016. CFA@UAI Frederick Eberhardt, Elias Bareinboim, Marloes H. Maathuis, Joris M. Mooij, Ricardo Silva
2017	—	`conf`	Tomography of the London Underground: a Scalable Model for Origin-Destination Data. NIPS Nicolò Colombo, Ricardo Silva, Soong Moon Kang
2017	B	`conf`	Visualization of Topic-Sentiment Dynamics in Crowdfunding Projects. IDA Rafael Augusto Ferreira do Carmo, Soong Moon Kang, Ricardo Silva
2017	—	`conf`	When Worlds Collide: Integrating Different Counterfactual Assumptions in Fairness. NIPS Chris Russell, Matt J. Kusner, Joshua R. Loftus, Ricardo Silva
2016	J	`jnl`	Causal Inference through a Witness Protection Program. J. Mach. Learn. Res. Ricardo Silva, Robin J. Evans
2016	—	`conf`	Observational-Interventional Priors for Dose-Response Learning. NIPS Ricardo Silva
2016	—	`conf`	Scaling Factorial Hidden Markov Models: Stochastic Variational Inference without Messages. NIPS Yin Cheng Ng, Pawel M. Chilinski, Ricardo Silva
2015	—	`ed.`	Proceedings of the UAI 2015 Workshop on Advances in Causal Inference co-located with the 31st Conference on Uncertainty in Artificial Intelligence (UAI 2015), Amsterdam, The Netherlands, July 16, 2015. ACI@UAI Ricardo Silva, Ilya Shpitser, Robin J. Evans, Jonas Peters, Tom Claassen
2014	—	`conf`	Causal Inference through a Witness Protection Program. NIPS Ricardo Silva, Robin J. Evans
2012	A	`conf`	Latent Composite Likelihood Learning for the Structured Canonical Correlation Model. UAI Ricardo Silva