Browse by UCL people
Group by: Type | Date
Number of items: 36.
Article
Bartlett, TE;
Kosmidis, I;
Silva, R;
(2021)
Two-way sparsity for time-varying networks, with applications in genomics.
Annals of Applied Statistics
, 15
(2)
pp. 856-879.
10.1214/20-AOAS1416.
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Coutrot, A;
Silva, R;
Manley, E;
de Cothi, W;
Sami, S;
Bohbot, VD;
Wiener, JM;
... Spiers, HJ; + view all
(2018)
Global Determinants of Navigation Ability.
Current Biology
, 28
(17)
2861-2866.e4.
10.1016/j.cub.2018.06.009.
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Dixon, WG;
Beukenhorst, AL;
Yimer, BB;
Cook, L;
Gasparrini, A;
El-Hay, T;
Hellman, B;
... McBeth, J; + view all
(2019)
How the weather affects the pain of citizen scientists using a smartphone app.
npj Digital Medicine
, 2
, Article 105. 10.1038/s41746-019-0180-3.
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Francisco, EDR;
Kugler, JL;
Kang, SM;
Silva, R;
Whigham, PA;
(2019)
Beyond Technology: Management Challenges in the Big Data Era.
Revista de Administração de Empresas
, 59
(6)
pp. 375-378.
10.1590/S0034-759020190603.
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Pagani, A;
Wei, Z;
Silva, R;
Guo, W;
(2022)
Neural Network Approximation of Graph Fourier Transform for Sparse Sampling of Networked Dynamics.
ACM Transactions on Internet Technology
, 22
(1)
, Article 21. 10.1145/3461838.
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Roa Vicens, J;
Chtourou, C;
Filos, A;
Rullan, F;
Gal, Y;
Silva, R;
(2019)
Towards Inverse Reinforcement Learning for Limit Order Book Dynamics.
arXiv
10.48550/arXiv.1906.04813.
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Silva, R;
Evans, R;
(2016)
Causal Inference through a Witness Protection Program.
Journal of Machine Learning Research
, 17
, Article 56.
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Silva, R;
Kalaitzis, A;
(2015)
Bayesian inference via projections.
Statistics and Computing
, 25
(4)
pp. 739-753.
10.1007/s11222-015-9557-6.
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Silva, R;
Kang, SM;
Airoldi, EM;
(2015)
Predicting traffic volumes and estimating the effects of shocks in massive transportation systems.
Proceedings of the National Academy of Sciences of the United States of America
, 112
(18)
pp. 5643-5648.
10.1073/pnas.1412908112.
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Silva, R;
Shimizu, S;
(2017)
Learning Instrumental Variables with Structural and Non-Gaussianity Assumptions.
Journal of Machine Learning Research
, 18
(120)
pp. 1-49.
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Whitaker, G;
Silva, R;
Edwards, D;
Kosmidis, I;
(2021)
A Bayesian approach for determining player abilities in football.
Journal of the Royal Statistical Society Series C: Applied Statistics
, 70
(1)
pp. 174-201.
10.1111/rssc.12454.
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Book
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Globerson, A and Silva, R (Eds).
(2018)
Uncertainty in Artificial Intelligence: Proceedings of the Thirty-Fourth Conference.
[Book].
AUAI Press: Corvallis, Oregon, USA.
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Proceedings paper
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Chilinski, P;
Silva, R;
(2020)
Neural Likelihoods via Cumulative Distribution Functions.
In: Peters, J and Sontag, D, (eds.)
Proceedings of Machine Learning Research.
(pp. pp. 420-429).
PMLR: Online conference.
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Colombo, N;
Silva, R;
Kang, SM;
(2017)
Tomography of the London Underground: a Scalable Model for Origin-Destination Data.
In: Guyon, I and Luxburg, UV and Bengio, S and Wallach, H and Fergus, R and Vishwanathan, S and Garnett, R, (eds.)
Advances in Neural Information Processing Systems 30 (NIPS 2017).
NIPS Proceedings: Long Beach, CA, USA.
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Do Carmo, RAF;
Kang, SM;
Silva, R;
(2017)
Visualization of Topic-Sentiment Dynamics in Crowdfunding Projects.
In: Adams, N and Tucker, A and Weston, D, (eds.)
Advances in Intelligent Data Analysis XVI: 16th International Symposium, IDA 2017, London, UK, October 26–28, 2017, Proceedings.
(pp. pp. 40-51).
Springer: Cham, Switzerland.
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Gultchin, L;
Kusner, M;
Kanade, V;
Silva, R;
(2020)
Differentiable Causal Backdoor Discovery.
In: Chiappa, S and Calandra, R, (eds.)
Proceedings of the International Conference on Artificial Intelligence and Statistics.
PMLR: Online Conference.
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Gultchin, L;
Watson, DS;
Kusner, MJ;
Silva, R;
(2021)
Operationalizing Complex Causes: A Pragmatic View of Mediation.
In:
Proceedings of the 38th International Conference on Machine Learning.
(pp. pp. 3875-3885).
MLResearchPress
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Kaddour, J;
ZHU, Y;
Liu, Q;
Kusner, M;
Silva, R;
(2021)
Causal Effect Inference for Structured Treatments.
In: Ranzato, M and Beygelzimer, A and Liang, PS and Vaughan, JW, (eds.)
Advances in Neural Information Processing Systems 34 pre-proceedings (NeurIPS 2021).
Neural Information Processing Systems (NeurIPS)
(In press).
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Kaddour, Jean;
Linqing, Liu;
Silva, Ricardo;
Kusner, Matt;
(2022)
When Do Flat Minima Optimizers Work?
In:
NeurIPS Proceedings.
NeurIPS: New Orleans, LA, USA.
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Kalaitzis, A;
Silva, R;
(2013)
Flexible sampling of discrete data correlations without the marginal distributions.
In: Burges, CJC and Bottou, L and Welling, M and Ghahramani, Z and Weinberger, KQ, (eds.)
Advances in Neural Information Processing Systems 26 (NIPS 2013).
Neural Information Processing Systems Foundation: Lake Tahoe, NV, USA.
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Kilbertus, N;
Ball, P;
Kusner, M;
Weller, A;
Silva, R;
(2019)
The Sensitivity of Counterfactual Fairness to Unmeasured Confounding.
In: Globerson, A and Silva, R, (eds.)
Proceedings of the 35th Uncertainty in Artificial Intelligence Conference (UAI 2019).
AUAI Press: Tel Aviv, Israel.
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Kilbertus, N;
Kusner, M;
Silva, R;
(2020)
A class of algorithms for general instrumental variable models.
In:
Proceedings of the Advances in Neural Information Processing Systems (NeurIPS 2020).
Advances in Neural Information Processing Systems (NeurIPS 2020): Virtual conference.
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Kusner, M;
Russell, C;
Loftus, J;
Silva, R;
(2019)
Making Decisions that Reduce Discriminatory Impacts.
In: Xing, E, (ed.)
Proceedings of the 36th International Conference on Machine Learning (IML 2019).
PMLR (Proceedings of Machine Learning Research): Long Beach, CA, USA.
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Kusner, MJ;
Russell, C;
Loftus, J;
Silva, R;
(2017)
Counterfactual Fairness.
In: Guyon, I and Luxburg, UV and Bengio, S and Wallach, H and Fergus, R and Vishwanathan, S and Garnett, R, (eds.)
Advances in Neural Information Processing Systems 30 (NIPS 2017).
NIPS Proceedings: Long Beach, CA, USA.
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Ng, YC;
Chilinski, P;
Silva, R;
(2016)
Scaling Factorial Hidden Markov Models: Stochastic Variational Inference without Messages.
In:
Proceedings of the 29th Conference on Neural Information Processing Systems (NIPS 2016).
Neural Information Processing Systems Foundation: Barcelona, Spain.
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Ng, YC;
Colombo, N;
Silva, R;
(2018)
Bayesian Semi-supervised Learning with Graph Gaussian Processes.
In: Bengio, S and Wallach, H and Larochelle, H and Grauman, K and CesaBianchi, N and Garnett, R, (eds.)
Neural Information Processing Systems 31.
Neural Information Processing Systems Foundation, Inc.: Montreal, Canada.
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Padh, Kirtan;
Zeitler, Jakob;
Watson, David;
Kusner, Matt;
Silva, Ricardo;
Kilbertus, Niki;
(2023)
Stochastic causal programming for bounding treatment effects.
In: Van der Schaar, Mihaela and Zhang, Cheng and Janzing, Dominik, (eds.)
Proceedings of the Second Conference on Causal Learning and Reasoning.
(pp. pp. 142-176).
Proceedings of Machine Learning Research (PMLR): Tübingen, Germany.
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Russell, C;
Kusner, M;
Loftus, C;
Silva, R;
(2017)
When Worlds Collide: Integrating Different Counterfactual Assumptions in Fairness.
In: Guyon, I and Luxburg, UV and Bengio, S and Wallach, H and Fergus, R and Vishwanathan, S and Garnett, R, (eds.)
Advances in Neural Information Processing Systems 30 (NIPS 2017).
NIPS Proceedings: Long Beach, CA, USA.
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Saengkyongam, S;
Silva, R;
(2020)
Learning Joint Nonlinear Effects from Single-variable Interventions in the Presence of Hidden Confounders.
In: Peters, J and Sontag, D, (eds.)
Proceedings of Machine Learning Research.
(pp. pp. 300-309).
PMLR: Online conference.
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Silva, Ricardo;
Bravo-Hermsdorff, Gecia;
Watson, David;
Yu, Jialin;
Zeitler, Jakob;
(2023)
Intervention Generalization: A View from Factor Graph Models.
In:
NeurIPS.
Proceedings of the 37th Conference on Neural Information Processing Systems (NeurIPS 2023).
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Silva, R;
(2016)
Observational-Interventional Priors for Dose-Response Learning.
In:
Proceedings of the Advances in Neural Information Processing Systems 29 (NIPS 2016).
NIPS Proceedings: Barcelona, Spain.
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Silva, R;
(2012)
Latent Composite Likelihood Learning for the Structured Canonical Correlation Model.
In:
Uncertainty in Artificial Intelligence: Proceedings of the Twenty-Eighth Conference (2012).
(pp. pp. 765-774).
AUAI Press: Corvallis, Oregon, USA.
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Silva, R;
Evans, R;
(2014)
Causal Inference through a Witness Protection Program.
In: Gharamani, Z and Welling, W and Cortes, C and Lawrence, ND and Weinberger, KQ, (eds.)
Advances in Neural Information Processing Systems 27 (NIPS 2014).
Neural Information Processing Systems Foundation: Montreal, Canada.
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Watson, DS;
Silva, R;
(2022)
Causal Discovery Under a Confounder Blanket.
In:
Proceedings of the 38th Conference on Uncertainty in Artificial Intelligence (UAI 2022).
(pp. pp. 2096-2106).
PMLR 180
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Working / discussion paper
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Loftus, JR;
Russell, C;
Kusner, MJ;
Silva, R;
(2018)
Causal Reasoning for Algorithmic Fairness.
ArXiv: Ithaca, NY, USA.
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Thesis
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Parsons, S;
(2016)
Approximation methods for latent variable models.
Doctoral thesis , UCL (University College London).
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