research
∙
06/11/2020
A Class of Algorithms for General Instrumental Variable Models
Causal treatment effect estimation is a key problem that arises in a var...
share
research
∙
05/23/2020
Learning Joint Nonlinear Effects from Single-variable Interventions in the Presence of Hidden Confounders
We propose an approach to estimate the effect of multiple simultaneous i...
research
∙
03/03/2020
Differentiable Causal Backdoor Discovery
Discovering the causal effect of a decision is critical to nearly all fo...
research
∙
02/11/2020
Neural Network Approximation of Graph Fourier Transforms for Sparse Sampling of Networked Flow Dynamics
Infrastructure monitoring is critical for safe operations and sustainabi...
research
∙
12/09/2019
Adversarial recovery of agent rewards from latent spaces of the limit order book
Inverse reinforcement learning has proved its ability to explain state-a...
research
∙
08/20/2019
Counterfactual Distribution Regression for Structured Inference
We consider problems in which a system receives external perturbations f...
research
∙
07/04/2019
Sharing and Learning Alloy on the Web
We present Alloy4Fun, a web application that enables online editing and ...
research
∙
07/01/2019
The Sensitivity of Counterfactual Fairness to Unmeasured Confounding
Causal approaches to fairness have seen substantial recent interest, bot...
research
∙
06/11/2019
Towards Inverse Reinforcement Learning for Limit Order Book Dynamics
Multi-agent learning is a promising method to simulate aggregate competi...
research
∙
11/02/2018
Neural Likelihoods via Cumulative Distribution Functions
We leverage neural networks as universal approximators of monotonic func...
research
∙
09/12/2018
Bayesian Semi-supervised Learning with Graph Gaussian Processes
We propose a data-efficient Gaussian process-based Bayesian approach to ...
research
∙
06/24/2018
Ethical Implications of Social Internet of Vehicles Systems
The core concept of IoT is to equip real world objects with computing, p...
research
∙
06/06/2018
Causal Interventions for Fairness
Most approaches in algorithmic fairness constrain machine learning metho...
research
∙
05/15/2018
Causal Reasoning for Algorithmic Fairness
In this work, we argue for the importance of causal reasoning in creatin...
research
∙
05/02/2018
Alpha-Beta Divergence For Variational Inference
This paper introduces a variational approximation framework using direct...
research
∙
02/23/2018
Modeling goal chances in soccer: a Bayesian inference approach
We consider the task of determining the number of chances a soccer team ...
research
∙
02/22/2018
Sparse Bayesian dynamic network models, with genomics applications
Network models have become an important topic in modern statistics, and ...
research
∙
10/11/2017
A Dynamic Edge Exchangeable Model for Sparse Temporal Networks
We propose a dynamic edge exchangeable network model that can capture sp...
research
∙
03/20/2017
Counterfactual Fairness
Machine learning can impact people with legal or ethical consequences wh...
research
∙
08/12/2016
Scaling Factorial Hidden Markov Models: Stochastic Variational Inference without Messages
Factorial Hidden Markov Models (FHMMs) are powerful models for sequentia...
research
∙
05/05/2016
Observational-Interventional Priors for Dose-Response Learning
Controlled interventions provide the most direct source of information f...
research
∙
11/09/2015
Bayesian Inference in Cumulative Distribution Fields
One approach for constructing copula functions is by multiplication. Giv...
research
∙
11/09/2015
Learning Instrumental Variables with Non-Gaussianity Assumptions: Theoretical Limitations and Practical Algorithms
Learning a causal effect from observational data is not straightforward,...
research
∙
08/09/2014
Gaussian Process Structural Equation Models with Latent Variables
In a variety of disciplines such as social sciences, psychology, medicin...
research
∙
06/12/2013
Flexible sampling of discrete data correlations without the marginal distributions
Learning the joint dependence of discrete variables is a fundamental pro...
research
∙
10/19/2012
Learning Measurement Models for Unobserved Variables
Observed associations in a database may be due in whole or part to varia...
research
∙
10/16/2012
Latent Composite Likelihood Learning for the Structured Canonical Correlation Model
Latent variable models are used to estimate variables of interest quanti...
research
∙
08/31/2010