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ORCIDRicardo Silva 0001
Person information
- affiliation: University College London, Department of Statistical Science, London, UK
- affiliation: The Alan Turing Institute, London, UK
- affiliation (PhD 2005): Carnegie Mellon University, Machine Learning Department, Pittsburgh, PA, USA
Other persons with the same name
- Ricardo Silva — disambiguation page
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2020 – today
- 2024
[c27]Limor Gultchin, Siyuan Guo, Alan Malek, Silvia Chiappa, Ricardo Silva:
Pragmatic Fairness: Developing Policies with Outcome Disparity Control. CLeaR 2024: 243-264- [c26]Kaican Li, Weiyan Xie, Yongxiang Huang, Didan Deng, Lanqing Hong, Zhenguo Li, Ricardo Silva, Nevin L. Zhang:
Dual Risk Minimization: Towards Next-Level Robustness in Fine-tuning Zero-Shot Models. NeurIPS 2024 - [c25]Jialin Yu, Andreas Koukorinis, Nicolò Colombo, Yuchen Zhu, Ricardo Silva:
Structured Learning of Compositional Sequential Interventions. NeurIPS 2024 - [c24]David S. Watson, Jordan Penn, Lee M. Gunderson, Gecia Bravo Hermsdorff, Afsaneh Mastouri, Ricardo Silva:
Bounding causal effects with leaky instruments. UAI 2024: 3689-3710 - [i31]Ricardo Silva:
Counterfactual Fairness Is Not Demographic Parity, and Other Observations. CoRR abs/2402.02663 (2024) - [i30]David S. Watson, Jordan Penn
, Lee M. Gunderson, Gecia Bravo Hermsdorff, Afsaneh Mastouri, Ricardo Silva:
Bounding Causal Effects with Leaky Instruments. CoRR abs/2404.04446 (2024) - [i29]Jialin Yu, Andreas Koukorinis, Nicolò Colombo, Yuchen Zhu, Ricardo Silva:
Structured Learning of Compositional Sequential Interventions. CoRR abs/2406.05745 (2024) - [i28]Jialin Yu, Yuxiang Zhou, Yulan He, Nevin L. Zhang, Ricardo Silva:
Fine-Tuning Pre-trained Language Models for Robust Causal Representation Learning. CoRR abs/2410.14375 (2024) - [i27]Kaican Li, Weiyan Xie, Yongxiang Huang, Didan Deng, Lanqing Hong, Zhenguo Li, Ricardo Silva, Nevin L. Zhang:
Dual Risk Minimization: Towards Next-Level Robustness in Fine-tuning Zero-Shot Models. CoRR abs/2411.19757 (2024) - 2023
- [c23]Kirtan Padh, Jakob Zeitler, David S. Watson, Matt J. Kusner, Ricardo Silva, Niki Kilbertus:
Stochastic Causal Programming for Bounding Treatment Effects. CLeaR 2023: 142-176 - [c22]Gecia Bravo Hermsdorff, David S. Watson, Jialin Yu, Jakob Zeitler, Ricardo Silva:
Intervention Generalization: A View from Factor Graph Models. NeurIPS 2023 - [i26]Limor Gultchin, Siyuan Guo, Alan Malek, Silvia Chiappa, Ricardo Silva:
Pragmatic Fairness: Developing Policies with Outcome Disparity Control. CoRR abs/2301.12278 (2023) - [i25]Aengus Lynch, Gbètondji J.-S. Dovonon, Jean Kaddour, Ricardo Silva:
Spawrious: A Benchmark for Fine Control of Spurious Correlation Biases. CoRR abs/2303.05470 (2023) - [i24]Gecia Bravo Hermsdorff, David S. Watson, Jialin Yu, Jakob Zeitler, Ricardo Silva:
Intervention Generalization: A View from Factor Graph Models. CoRR abs/2306.04027 (2023) - 2022
- [j5]Alessio Pagani, Zhuangkun Wei, Ricardo Silva, Weisi Guo:
Neural Network Approximation of Graph Fourier Transform for Sparse Sampling of Networked Dynamics. ACM Trans. Internet Techn. 22(1): 21:1-21:18 (2022) - [c21]Jacobo Roa-Vicens, Yao Lei Xu, Ricardo Silva, Danilo P. Mandic:
Graph and tensor-train recurrent neural networks for high-dimensional models of limit order books. ICAIF 2022: 207-213 - [c20]Jean Kaddour, Linqing Liu, Ricardo Silva, Matt J. Kusner:
When Do Flat Minima Optimizers Work? NeurIPS 2022 - [c19]David S. Watson, Ricardo Silva:
Causal discovery under a confounder blanket. UAI 2022: 2096-2106 - [c18]Yuchen Zhu, Limor Gultchin, Arthur Gretton, Matt J. Kusner, Ricardo Silva:
Causal inference with treatment measurement error: a nonparametric instrumental variable approach. UAI 2022: 2414-2424 - [i23]Jean Kaddour, Linqing Liu, Ricardo Silva, Matt J. Kusner:
Questions for Flat-Minima Optimization of Modern Neural Networks. CoRR abs/2202.00661 (2022) - [i22]Kirtan Padh, Jakob Zeitler, David S. Watson, Matt J. Kusner, Ricardo Silva, Niki Kilbertus:
Stochastic Causal Programming for Bounding Treatment Effects. CoRR abs/2202.10806 (2022) - [i21]Jakob Zeitler, Ricardo Silva:
The Causal Marginal Polytope for Bounding Treatment Effects. CoRR abs/2202.13851 (2022) - [i20]David S. Watson, Ricardo Silva:
Causal discovery under a confounder blanket. CoRR abs/2205.05715 (2022) - [i19]Yuchen Zhu, Limor Gultchin, Arthur Gretton, Matt J. Kusner, Ricardo Silva:
Causal Inference with Treatment Measurement Error: A Nonparametric Instrumental Variable Approach. CoRR abs/2206.09186 (2022) - [i18]Jean Kaddour, Aengus Lynch, Qi Liu, Matt J. Kusner, Ricardo Silva:
Causal Machine Learning: A Survey and Open Problems. CoRR abs/2206.15475 (2022) - 2021
- [c17]Limor Gultchin, David S. Watson, Matt J. Kusner, Ricardo Silva:
Operationalizing Complex Causes: A Pragmatic View of Mediation. ICML 2021: 3875-3885 - [c16]Afsaneh Mastouri, Yuchen Zhu, Limor Gultchin, Anna Korba, Ricardo Silva, Matt J. Kusner, Arthur Gretton, Krikamol Muandet:
Proximal Causal Learning with Kernels: Two-Stage Estimation and Moment Restriction. ICML 2021: 7512-7523 - [c15]Jean Kaddour, Yuchen Zhu, Qi Liu, Matt J. Kusner, Ricardo Silva:
Causal Effect Inference for Structured Treatments. NeurIPS 2021: 24841-24854 - [i17]Afsaneh Mastouri, Yuchen Zhu, Limor Gultchin, Anna Korba, Ricardo Silva, Matt J. Kusner, Arthur Gretton, Krikamol Muandet:
Proximal Causal Learning with Kernels: Two-Stage Estimation and Moment Restriction. CoRR abs/2105.04544 (2021) - [i16]Jean Kaddour, Qi Liu, Yuchen Zhu, Matt J. Kusner, Ricardo Silva:
Graph Intervention Networks for Causal Effect Estimation. CoRR abs/2106.01939 (2021) - [i15]Limor Gultchin, David S. Watson, Matt J. Kusner, Ricardo Silva:
Operationalizing Complex Causes: A Pragmatic View of Mediation. CoRR abs/2106.05074 (2021) - 2020
- [c14]Limor Gultchin, Matt J. Kusner, Varun Kanade, Ricardo Silva:
Differentiable Causal Backdoor Discovery. AISTATS 2020: 3970-3979 - [c13]Niki Kilbertus, Matt J. Kusner, Ricardo Silva:
A Class of Algorithms for General Instrumental Variable Models. NeurIPS 2020 - [c12]Pawel M. Chilinski, Ricardo Silva:
Neural Likelihoods via Cumulative Distribution Functions. UAI 2020: 420-429 - [i14]Alessio Pagani, Zhuangkun Wei, Ricardo Silva, Weisi Guo:
Neural Network Approximation of Graph Fourier Transforms for Sparse Sampling of Networked Flow Dynamics. CoRR abs/2002.05508 (2020) - [i13]Limor Gultchin, Matt J. Kusner, Varun Kanade, Ricardo Silva:
Differentiable Causal Backdoor Discovery. CoRR abs/2003.01461 (2020) - [i12]Niki Kilbertus, Matt J. Kusner, Ricardo Silva:
A Class of Algorithms for General Instrumental Variable Models. CoRR abs/2006.06366 (2020)
2010 – 2019
- 2019
- [j4]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:
How the weather affects the pain of citizen scientists using a smartphone app. npj Digit. Medicine 2 (2019) - [c11]Matt J. Kusner, Chris Russell, Joshua R. Loftus, Ricardo Silva:
Making Decisions that Reduce Discriminatory Impacts. ICML 2019: 3591-3600 - [c10]Niki Kilbertus, Philip J. Ball, Matt J. Kusner, Adrian Weller, Ricardo Silva:
The Sensitivity of Counterfactual Fairness to Unmeasured Confounding. UAI 2019: 616-626 - [e4]Amir Globerson, Ricardo Silva:
Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, UAI 2019, Tel Aviv, Israel, July 22-25, 2019. Proceedings of Machine Learning Research 115, AUAI Press 2019 [contents] - [i11]Jacobo Roa-Vicens, Cyrine Chtourou, Angelos Filos, Francisco Rullan, Yarin Gal, Ricardo Silva:
Towards Inverse Reinforcement Learning for Limit Order Book Dynamics. CoRR abs/1906.04813 (2019) - [i10]Niki Kilbertus, Philip J. Ball, Matt J. Kusner, Adrian Weller, Ricardo Silva:
The Sensitivity of Counterfactual Fairness to Unmeasured Confounding. CoRR abs/1907.01040 (2019) - [i9]Nicolò Colombo, Ricardo Silva, Soong Moon Kang, Arthur Gretton:
Counterfactual Distribution Regression for Structured Inference. CoRR abs/1908.07193 (2019) - [i8]Jacobo Roa-Vicens, Yuanbo Wang, Virgile Mison, Yarin Gal, Ricardo Silva:
Adversarial recovery of agent rewards from latent spaces of the limit order book. CoRR abs/1912.04242 (2019) - 2018
- [j3]Gavin A. Whitaker, Ricardo Silva, Daniel Edwards:
Visualizing a Team's Goal Chances in Soccer from Attacking Events: A Bayesian Inference Approach. Big Data 6(4): 271-290 (2018) - [c9]Yin Cheng Ng, Nicolò Colombo, Ricardo Silva:
Bayesian Semi-supervised Learning with Graph Gaussian Processes. NeurIPS 2018: 1690-1701 - [e3]Amir Globerson, Ricardo Silva:
Proceedings of the Thirty-Fourth Conference on Uncertainty in Artificial Intelligence, UAI 2018, Monterey, California, USA, August 6-10, 2018. AUAI Press 2018 [contents] - [i7]Jean-Baptiste Regli, Ricardo Silva:
Alpha-Beta Divergence For Variational Inference. CoRR abs/1805.01045 (2018) - [i6]Joshua R. Loftus, Chris Russell, Matt J. Kusner, Ricardo Silva:
Causal Reasoning for Algorithmic Fairness. CoRR abs/1805.05859 (2018) - [i5]Matt J. Kusner, Chris Russell, Joshua R. Loftus, Ricardo Silva:
Causal Interventions for Fairness. CoRR abs/1806.02380 (2018) - [i4]Yin Cheng Ng, Ricardo Silva:
Bayesian Semi-supervised Learning with Graph Gaussian Processes. CoRR abs/1809.04379 (2018) - [i3]Pawel M. Chilinski, Ricardo Silva:
Neural Likelihoods via Cumulative Distribution Functions. CoRR abs/1811.00974 (2018) - 2017
- [j2]Ricardo Silva, Shohei Shimizu:
Learning Instrumental Variables with Structural and Non-Gaussianity Assumptions. J. Mach. Learn. Res. 18: 120:1-120:49 (2017) - [c8]Rafael Augusto Ferreira do Carmo, Soong Moon Kang, Ricardo Silva:
Visualization of Topic-Sentiment Dynamics in Crowdfunding Projects. IDA 2017: 40-51 - [c7]Nicolò Colombo, Ricardo Silva, Soong Moon Kang:
Tomography of the London Underground: a Scalable Model for Origin-Destination Data. NIPS 2017: 3062-3073 - [c6]Matt J. Kusner, Joshua R. Loftus, Chris Russell, Ricardo Silva:
Counterfactual Fairness. NIPS 2017: 4066-4076 - [c5]Chris Russell, Matt J. Kusner, Joshua R. Loftus, Ricardo Silva:
When Worlds Collide: Integrating Different Counterfactual Assumptions in Fairness. NIPS 2017: 6414-6423 - [e2]Frederick Eberhardt, Elias Bareinboim, Marloes H. Maathuis, Joris M. Mooij, Ricardo Silva:
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. CEUR Workshop Proceedings 1792, CEUR-WS.org 2017 [contents] - [r1]Ricardo Silva:
Causality. Encyclopedia of Machine Learning and Data Mining 2017: 194-202 - [i2]Matt J. Kusner, Joshua R. Loftus, Chris Russell, Ricardo Silva:
Counterfactual Fairness. CoRR abs/1703.06856 (2017) - [i1]Yin Cheng Ng, Ricardo Silva:
A Dynamic Edge Exchangeable Model for Sparse Temporal Networks. CoRR abs/1710.04008 (2017) - 2016
- [j1]Ricardo Silva, Robin J. Evans:
Causal Inference through a Witness Protection Program. J. Mach. Learn. Res. 17: 56:1-56:53 (2016) - [c4]Ricardo Silva:
Observational-Interventional Priors for Dose-Response Learning. NIPS 2016: 1561-1569 - [c3]Yin Cheng Ng, Pawel M. Chilinski, Ricardo Silva:
Scaling Factorial Hidden Markov Models: Stochastic Variational Inference without Messages. NIPS 2016: 4044-4052 - 2015
- [e1]Ricardo Silva, Ilya Shpitser, Robin J. Evans, Jonas Peters, Tom Claassen:
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. CEUR Workshop Proceedings 1504, CEUR-WS.org 2015 [contents] - 2014
- [c2]Ricardo Silva, Robin J. Evans:
Causal Inference through a Witness Protection Program. NIPS 2014: 298-306 - 2012
- [c1]Ricardo Silva:
Latent Composite Likelihood Learning for the Structured Canonical Correlation Model. UAI 2012: 765-774
Coauthor Index
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