
Neural Likelihoods via Cumulative Distribution Functions
We leverage neural networks as universal approximators of monotonic func...
11/02/2018 ∙ by Pawel Chilinski, et al. ∙ 6 ∙ shareread it

The Sensitivity of Counterfactual Fairness to Unmeasured Confounding
Causal approaches to fairness have seen substantial recent interest, bot...
07/01/2019 ∙ by Niki Kilbertus, et al. ∙ 5 ∙ shareread it

Counterfactual Distribution Regression for Structured Inference
We consider problems in which a system receives external perturbations f...
08/20/2019 ∙ by Nicolo Colombo, et al. ∙ 5 ∙ shareread it

Towards Inverse Reinforcement Learning for Limit Order Book Dynamics
Multiagent learning is a promising method to simulate aggregate competi...
06/11/2019 ∙ by Jacobo RoaVicens, et al. ∙ 1 ∙ shareread it

A Dynamic Edge Exchangeable Model for Sparse Temporal Networks
We propose a dynamic edge exchangeable network model that can capture sp...
10/11/2017 ∙ by Yin Cheng Ng, et al. ∙ 0 ∙ shareread it

Counterfactual Fairness
Machine learning can impact people with legal or ethical consequences wh...
03/20/2017 ∙ by Matt J. Kusner, et al. ∙ 0 ∙ shareread it

Scaling Factorial Hidden Markov Models: Stochastic Variational Inference without Messages
Factorial Hidden Markov Models (FHMMs) are powerful models for sequentia...
08/12/2016 ∙ by Yin Cheng Ng, et al. ∙ 0 ∙ shareread it

ObservationalInterventional Priors for DoseResponse Learning
Controlled interventions provide the most direct source of information f...
05/05/2016 ∙ by Ricardo Silva, et al. ∙ 0 ∙ shareread it

Bayesian Inference in Cumulative Distribution Fields
One approach for constructing copula functions is by multiplication. Giv...
11/09/2015 ∙ by Ricardo Silva, et al. ∙ 0 ∙ shareread it

Learning Instrumental Variables with NonGaussianity Assumptions: Theoretical Limitations and Practical Algorithms
Learning a causal effect from observational data is not straightforward,...
11/09/2015 ∙ by Ricardo Silva, et al. ∙ 0 ∙ shareread it

Gaussian Process Structural Equation Models with Latent Variables
In a variety of disciplines such as social sciences, psychology, medicin...
08/09/2014 ∙ by Ricardo Silva, et al. ∙ 0 ∙ shareread it

Flexible sampling of discrete data correlations without the marginal distributions
Learning the joint dependence of discrete variables is a fundamental pro...
06/12/2013 ∙ by Alfredo Kalaitzis, et al. ∙ 0 ∙ shareread it

Learning Measurement Models for Unobserved Variables
Observed associations in a database may be due in whole or part to varia...
10/19/2012 ∙ by Ricardo Silva, et al. ∙ 0 ∙ shareread it

Latent Composite Likelihood Learning for the Structured Canonical Correlation Model
Latent variable models are used to estimate variables of interest quanti...
10/16/2012 ∙ by Ricardo Silva, et al. ∙ 0 ∙ shareread it

Mixed Cumulative Distribution Networks
Directed acyclic graphs (DAGs) are a popular framework to express multiv...
08/31/2010 ∙ by Ricardo Silva, et al. ∙ 0 ∙ shareread it

Sparse Bayesian dynamic network models, with genomics applications
Network models have become an important topic in modern statistics, and ...
02/22/2018 ∙ by Thomas E. Bartlett, et al. ∙ 0 ∙ shareread it

AlphaBeta Divergence For Variational Inference
This paper introduces a variational approximation framework using direct...
05/02/2018 ∙ by JeanBaptiste Regli, et al. ∙ 0 ∙ shareread it

Modeling goal chances in soccer: a Bayesian inference approach
We consider the task of determining the number of chances a soccer team ...
02/23/2018 ∙ by Gavin A. Whitaker, et al. ∙ 0 ∙ shareread it

Causal Interventions for Fairness
Most approaches in algorithmic fairness constrain machine learning metho...
06/06/2018 ∙ by Matt J. Kusner, et al. ∙ 0 ∙ shareread it

Causal Reasoning for Algorithmic Fairness
In this work, we argue for the importance of causal reasoning in creatin...
05/15/2018 ∙ by Joshua R. Loftus, et al. ∙ 0 ∙ shareread it

Ethical Implications of Social Internet of Vehicles Systems
The core concept of IoT is to equip real world objects with computing, p...
06/24/2018 ∙ by Ricardo Silva, et al. ∙ 0 ∙ shareread it

Bayesian Semisupervised Learning with Graph Gaussian Processes
We propose a dataefficient Gaussian processbased Bayesian approach to ...
09/12/2018 ∙ by Yin Cheng Ng, et al. ∙ 0 ∙ shareread it

Sharing and Learning Alloy on the Web
We present Alloy4Fun, a web application that enables online editing and ...
07/04/2019 ∙ by Nuno Macedo, et al. ∙ 0 ∙ shareread it