
Treatment Effect Estimation using Invariant Risk Minimization
Inferring causal individual treatment effect (ITE) from observational da...
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Optimal Policies for the Homogeneous Selective Labels Problem
Selective labels are a common feature of consequential decisionmaking a...
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DAGs with No Fears: A Closer Look at Continuous Optimization for Learning Bayesian Networks
This paper reexamines a continuous optimization framework dubbed NOTEAR...
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Deciding Fast and Slow: The Role of Cognitive Biases in AIassisted Decisionmaking
Several strands of research have aimed to bridge the gap between artific...
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ConsumerDriven Explanations for Machine Learning Decisions: An Empirical Study of Robustness
Many proposed methods for explaining machine learning predictions are in...
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An InformationTheoretic Perspective on the Relationship Between Fairness and Accuracy
Our goal is to understand the socalled tradeoff between fairness and a...
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One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques
As artificial intelligence and machine learning algorithms make further ...
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Characterization of Overlap in Observational Studies
Overlap between treatment groups is required for nonparametric estimatio...
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Teaching AI to Explain its Decisions Using Embeddings and MultiTask Learning
Using machine learning in highstakes applications often requires predic...
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Generalized Linear Rule Models
This paper considers generalized linear models using rulebased features...
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Optimized Score Transformation for Fair Classification
This paper considers fair probabilistic classification where the outputs...
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Interpretable Subgroup Discovery in Treatment Effect Estimation with Application to Opioid Prescribing Guidelines
The dearth of prescribing guidelines for physicians is one key driver of...
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TED: Teaching AI to Explain its Decisions
Artificial intelligence systems are being increasingly deployed due to t...
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Teaching Meaningful Explanations
The adoption of machine learning in highstakes applications such as hea...
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Boolean Decision Rules via Column Generation
This paper considers the learning of Boolean rules in either disjunctive...
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On the Supermodularity of Active Graphbased Semisupervised Learning with Stieltjes Matrix Regularization
Active graphbased semisupervised learning (AGSSL) aims to select a sm...
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DistributionPreserving kAnonymity
Preserving the privacy of individuals by protecting their sensitive attr...
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An EndToEnd Machine Learning Pipeline That Ensures Fairness Policies
In consequential realworld applications, machine learning (ML) based sy...
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Optimized Data PreProcessing for Discrimination Prevention
Nondiscrimination is a recognized objective in algorithmic decision mak...
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Interpretable Twolevel Boolean Rule Learning for Classification
This paper proposes algorithms for learning twolevel Boolean rules in C...
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Statistical Estimation and Clustering of Groupinvariant Orientation Parameters
We treat the problem of estimation of orientation parameters whose value...
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A Dictionary Approach to EBSD Indexing
We propose a framework for indexing of grain and subgrain structures in...
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Coercive Regionlevel Registration for Multimodal Images
We propose a coercive approach to simultaneously register and segment mu...
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Parameter estimation in spherical symmetry groups
This paper considers statistical estimation problems where the probabili...
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Marginal Likelihoods for Distributed Parameter Estimation of Gaussian Graphical Models
We consider distributed estimation of the inverse covariance matrix, als...
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Dennis Wei
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