
A Class of Algorithms for General Instrumental Variable Models
Causal treatment effect estimation is a key problem that arises in a var...
read it

Learning Joint Nonlinear Effects from Singlevariable Interventions in the Presence of Hidden Confounders
We propose an approach to estimate the effect of multiple simultaneous i...
read it

Differentiable Causal Backdoor Discovery
Discovering the causal effect of a decision is critical to nearly all fo...
read it

Neural Network Approximation of Graph Fourier Transforms for Sparse Sampling of Networked Flow Dynamics
Infrastructure monitoring is critical for safe operations and sustainabi...
read it

Adversarial recovery of agent rewards from latent spaces of the limit order book
Inverse reinforcement learning has proved its ability to explain statea...
read it

Counterfactual Distribution Regression for Structured Inference
We consider problems in which a system receives external perturbations f...
read it

Sharing and Learning Alloy on the Web
We present Alloy4Fun, a web application that enables online editing and ...
read it

The Sensitivity of Counterfactual Fairness to Unmeasured Confounding
Causal approaches to fairness have seen substantial recent interest, bot...
read it

Towards Inverse Reinforcement Learning for Limit Order Book Dynamics
Multiagent learning is a promising method to simulate aggregate competi...
read it

Neural Likelihoods via Cumulative Distribution Functions
We leverage neural networks as universal approximators of monotonic func...
read it

Bayesian Semisupervised Learning with Graph Gaussian Processes
We propose a dataefficient Gaussian processbased Bayesian approach to ...
read it

Ethical Implications of Social Internet of Vehicles Systems
The core concept of IoT is to equip real world objects with computing, p...
read it

Causal Interventions for Fairness
Most approaches in algorithmic fairness constrain machine learning metho...
read it

Causal Reasoning for Algorithmic Fairness
In this work, we argue for the importance of causal reasoning in creatin...
read it

AlphaBeta Divergence For Variational Inference
This paper introduces a variational approximation framework using direct...
read it

Modeling goal chances in soccer: a Bayesian inference approach
We consider the task of determining the number of chances a soccer team ...
read it

Sparse Bayesian dynamic network models, with genomics applications
Network models have become an important topic in modern statistics, and ...
read it

A Dynamic Edge Exchangeable Model for Sparse Temporal Networks
We propose a dynamic edge exchangeable network model that can capture sp...
read it

Counterfactual Fairness
Machine learning can impact people with legal or ethical consequences wh...
read it

Scaling Factorial Hidden Markov Models: Stochastic Variational Inference without Messages
Factorial Hidden Markov Models (FHMMs) are powerful models for sequentia...
read it

ObservationalInterventional Priors for DoseResponse Learning
Controlled interventions provide the most direct source of information f...
read it

Bayesian Inference in Cumulative Distribution Fields
One approach for constructing copula functions is by multiplication. Giv...
read it

Learning Instrumental Variables with NonGaussianity Assumptions: Theoretical Limitations and Practical Algorithms
Learning a causal effect from observational data is not straightforward,...
read it

Gaussian Process Structural Equation Models with Latent Variables
In a variety of disciplines such as social sciences, psychology, medicin...
read it

Flexible sampling of discrete data correlations without the marginal distributions
Learning the joint dependence of discrete variables is a fundamental pro...
read it

Learning Measurement Models for Unobserved Variables
Observed associations in a database may be due in whole or part to varia...
read it

Latent Composite Likelihood Learning for the Structured Canonical Correlation Model
Latent variable models are used to estimate variables of interest quanti...
read it

Mixed Cumulative Distribution Networks
Directed acyclic graphs (DAGs) are a popular framework to express multiv...
read it