
Neural Likelihoods via Cumulative Distribution Functions
We leverage neural networks as universal approximators of monotonic func...
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Adversarial recovery of agent rewards from latent spaces of the limit order book
Inverse reinforcement learning has proved its ability to explain statea...
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The Sensitivity of Counterfactual Fairness to Unmeasured Confounding
Causal approaches to fairness have seen substantial recent interest, bot...
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Counterfactual Distribution Regression for Structured Inference
We consider problems in which a system receives external perturbations f...
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Towards Inverse Reinforcement Learning for Limit Order Book Dynamics
Multiagent learning is a promising method to simulate aggregate competi...
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A Dynamic Edge Exchangeable Model for Sparse Temporal Networks
We propose a dynamic edge exchangeable network model that can capture sp...
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Counterfactual Fairness
Machine learning can impact people with legal or ethical consequences wh...
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Scaling Factorial Hidden Markov Models: Stochastic Variational Inference without Messages
Factorial Hidden Markov Models (FHMMs) are powerful models for sequentia...
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ObservationalInterventional Priors for DoseResponse Learning
Controlled interventions provide the most direct source of information f...
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Bayesian Inference in Cumulative Distribution Fields
One approach for constructing copula functions is by multiplication. Giv...
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Learning Instrumental Variables with NonGaussianity Assumptions: Theoretical Limitations and Practical Algorithms
Learning a causal effect from observational data is not straightforward,...
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Gaussian Process Structural Equation Models with Latent Variables
In a variety of disciplines such as social sciences, psychology, medicin...
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Flexible sampling of discrete data correlations without the marginal distributions
Learning the joint dependence of discrete variables is a fundamental pro...
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Learning Measurement Models for Unobserved Variables
Observed associations in a database may be due in whole or part to varia...
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Latent Composite Likelihood Learning for the Structured Canonical Correlation Model
Latent variable models are used to estimate variables of interest quanti...
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Mixed Cumulative Distribution Networks
Directed acyclic graphs (DAGs) are a popular framework to express multiv...
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Sparse Bayesian dynamic network models, with genomics applications
Network models have become an important topic in modern statistics, and ...
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AlphaBeta Divergence For Variational Inference
This paper introduces a variational approximation framework using direct...
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Modeling goal chances in soccer: a Bayesian inference approach
We consider the task of determining the number of chances a soccer team ...
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Causal Interventions for Fairness
Most approaches in algorithmic fairness constrain machine learning metho...
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Causal Reasoning for Algorithmic Fairness
In this work, we argue for the importance of causal reasoning in creatin...
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Ethical Implications of Social Internet of Vehicles Systems
The core concept of IoT is to equip real world objects with computing, p...
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Bayesian Semisupervised Learning with Graph Gaussian Processes
We propose a dataefficient Gaussian processbased Bayesian approach to ...
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Sharing and Learning Alloy on the Web
We present Alloy4Fun, a web application that enables online editing and ...
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Neural Network Approximation of Graph Fourier Transforms for Sparse Sampling of Networked Flow Dynamics
Infrastructure monitoring is critical for safe operations and sustainabi...
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Differentiable Causal Backdoor Discovery
Discovering the causal effect of a decision is critical to nearly all fo...
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