| Neural Network Approximation of Graph Fourier Transform for Sparse Sampling of Networked Dynamics | 0 | 0.34 | 2022 |
| Causal inference with treatment measurement error: a nonparametric instrumental variable approach. | 0 | 0.34 | 2022 |
| Proximal Causal Learning with Kernels: Two-Stage Estimation and Moment Restriction | 0 | 0.34 | 2021 |
| Operationalizing Complex Causes: A Pragmatic View Of Mediation | 0 | 0.34 | 2021 |
| A Class Of Algorithms For General Instrumental Variable Models | 0 | 0.34 | 2020 |
| Learning Joint Nonlinear Effects from Single-variable Interventions in the Presence of Hidden Confounders | 0 | 0.34 | 2020 |
| Towards Inverse Reinforcement Learning for Limit Order Book Dynamics. | 0 | 0.34 | 2019 |
| Making Decisions that Reduce Discriminatory Impacts | 0 | 0.34 | 2019 |
| The Sensitivity of Counterfactual Fairness to Unmeasured Confounding. | 1 | 0.36 | 2019 |
| Neural Likelihoods via Cumulative Distribution Functions. | 0 | 0.34 | 2018 |
| Alpha-Beta Divergence For Variational Inference. | 0 | 0.34 | 2018 |
| Causal Reasoning for Algorithmic Fairness. | 1 | 0.36 | 2018 |
| Visualizing a Team's Goal Chances in Soccer from Attacking Events: A Bayesian Inference Approach. | 0 | 0.34 | 2018 |
| Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, UAI 2019, Tel Aviv, Israel, July 22-25, 2019 | 0 | 0.34 | 2018 |
| Bayesian Semi-supervised Learning with Graph Gaussian Processes. | 1 | 0.34 | 2018 |
| Causal Interventions for Fairness. | 1 | 0.36 | 2018 |
| Visualization of Topic-Sentiment Dynamics in Crowdfunding Projects. | 0 | 0.34 | 2017 |
| A Dynamic Edge Exchangeable Model for Sparse Temporal Networks. | 1 | 0.38 | 2017 |
| Counterfactual Fairness. | 0 | 0.34 | 2017 |
| When Worlds Collide: Integrating Different Counterfactual Assumptions in Fairness. | 11 | 0.71 | 2017 |
| Learning Instrumental Variables with Structural and Non-Gaussianity Assumptions. | 0 | 0.34 | 2017 |
| Tomography of the London Underground: a Scalable Model for Origin-Destination Data. | 0 | 0.34 | 2017 |
| Observational-Interventional Priors for Dose-Response Learning. | 0 | 0.34 | 2016 |
| Scaling Factorial Hidden Markov Models: Stochastic Variational Inference without Messages. | 0 | 0.34 | 2016 |
| Bayesian inference via projections | 0 | 0.34 | 2015 |
| Causal Inference through a Witness Protection Program. | 3 | 0.42 | 2014 |
| Flexible Sampling for the Gaussian Copula Extended Rank Likelihood Model. | 0 | 0.34 | 2013 |
| Flexible sampling of discrete data correlations without the marginal distributions. | 6 | 0.53 | 2013 |
| Latent Composite Likelihood Learning for the Structured Canonical Correlation Model | 1 | 0.37 | 2012 |
| Discussion of "Learning Equivalence Classes of Acyclic Models with Latent and Selection Variables from Multiple Datasets with Overlapping Variables". | 0 | 0.34 | 2011 |
| Thinning Measurement Models and Questionnaire Design. | 0 | 0.34 | 2011 |
| Mixed Cumulative Distribution Networks | 5 | 0.69 | 2010 |
| Measuring Latent Causal Structure | 0 | 0.34 | 2010 |
| Gaussian Process Structural Equation Models with Latent Variables. | 0 | 0.34 | 2010 |
| Ranking relations using analogies in biological and information networks | 3 | 0.40 | 2009 |
| MCMC Methods for Bayesian Mixtures of Copulas | 1 | 0.63 | 2009 |
| Factorial Mixture of Gaussians and the Marginal Independence Model | 4 | 0.62 | 2009 |
| Learning the Structure of Linear Latent Variable Models | 55 | 5.69 | 2006 |
| Towards association rules with hidden variables | 0 | 0.34 | 2006 |
| Bayesian learning of measurement and structural models | 4 | 0.50 | 2006 |
| New d-separation identification results for learning continuous latent variable models | 2 | 1.11 | 2005 |
| Learning measurement models for unobserved variables | 8 | 1.60 | 2003 |
| Classification and filtering of spectra: A case study in mineralogy | 0 | 0.34 | 2002 |
| Hybrid systems of local basis functions | 0 | 0.34 | 2001 |
| Obtaining Simplified Rule Bases by Hybrid Learning | 1 | 0.35 | 2000 |
