Dennis Wei

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Treatment Effect Estimation using Invariant Risk Minimization

Inferring causal individual treatment effect (ITE) from observational da...
03/13/2021 ∙ by Abhin Shah, et al. ∙ 19 ∙ share

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Optimal Policies for the Homogeneous Selective Labels Problem

Selective labels are a common feature of consequential decision-making a...
11/02/2020 ∙ by Dennis Wei, et al. ∙ 0 ∙ share

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DAGs with No Fears: A Closer Look at Continuous Optimization for Learning Bayesian Networks

This paper re-examines a continuous optimization framework dubbed NOTEAR...
10/18/2020 ∙ by Dennis Wei, et al. ∙ 0 ∙ share

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Deciding Fast and Slow: The Role of Cognitive Biases in AI-assisted Decision-making

Several strands of research have aimed to bridge the gap between artific...
10/15/2020 ∙ by Charvi Rastogi, et al. ∙ 0 ∙ share

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Consumer-Driven Explanations for Machine Learning Decisions: An Empirical Study of Robustness

Many proposed methods for explaining machine learning predictions are in...
01/13/2020 ∙ by Michael Hind, et al. ∙ 0 ∙ share

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An Information-Theoretic Perspective on the Relationship Between Fairness and Accuracy

Our goal is to understand the so-called trade-off between fairness and a...
10/17/2019 ∙ by Sanghamitra Dutta, et al. ∙ 29 ∙ share

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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 ...
09/06/2019 ∙ by Vijay Arya, et al. ∙ 36 ∙ share

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Characterization of Overlap in Observational Studies

Overlap between treatment groups is required for nonparametric estimatio...
07/09/2019 ∙ by Fredrik D. Johansson, et al. ∙ 5 ∙ share

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Teaching AI to Explain its Decisions Using Embeddings and Multi-Task Learning

Using machine learning in high-stakes applications often requires predic...
06/05/2019 ∙ by Noel C. F. Codella, et al. ∙ 4 ∙ share

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Generalized Linear Rule Models

This paper considers generalized linear models using rule-based features...
06/05/2019 ∙ by Dennis Wei, et al. ∙ 0 ∙ share

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Optimized Score Transformation for Fair Classification

This paper considers fair probabilistic classification where the outputs...
05/31/2019 ∙ by Dennis Wei, et al. ∙ 0 ∙ share

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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...
05/08/2019 ∙ by Chirag Nagpal, et al. ∙ 11 ∙ share

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TED: Teaching AI to Explain its Decisions

Artificial intelligence systems are being increasingly deployed due to t...
11/12/2018 ∙ by Noel C. F. Codella, et al. ∙ 12 ∙ share

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Teaching Meaningful Explanations

The adoption of machine learning in high-stakes applications such as hea...
05/29/2018 ∙ by Noel C. F. Codella, et al. ∙ 0 ∙ share

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Boolean Decision Rules via Column Generation

This paper considers the learning of Boolean rules in either disjunctive...
05/24/2018 ∙ by Sanjeeb Dash, et al. ∙ 0 ∙ share

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On the Supermodularity of Active Graph-based Semi-supervised Learning with Stieltjes Matrix Regularization

Active graph-based semi-supervised learning (AG-SSL) aims to select a sm...
04/09/2018 ∙ by Pin-Yu Chen, et al. ∙ 0 ∙ share

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Distribution-Preserving k-Anonymity

Preserving the privacy of individuals by protecting their sensitive attr...
11/05/2017 ∙ by Dennis Wei, et al. ∙ 0 ∙ share

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An End-To-End Machine Learning Pipeline That Ensures Fairness Policies

In consequential real-world applications, machine learning (ML) based sy...
10/18/2017 ∙ by Samiulla Shaikh, et al. ∙ 0 ∙ share

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Optimized Data Pre-Processing for Discrimination Prevention

Non-discrimination is a recognized objective in algorithmic decision mak...
04/11/2017 ∙ by Flavio P. Calmon, et al. ∙ 0 ∙ share

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Interpretable Two-level Boolean Rule Learning for Classification

This paper proposes algorithms for learning two-level Boolean rules in C...
11/23/2015 ∙ by Guolong Su, et al. ∙ 0 ∙ share

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Statistical Estimation and Clustering of Group-invariant Orientation Parameters

We treat the problem of estimation of orientation parameters whose value...
03/15/2015 ∙ by Yu-Hui Chen, et al. ∙ 0 ∙ share

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A Dictionary Approach to EBSD Indexing

We propose a framework for indexing of grain and sub-grain structures in...
02/26/2015 ∙ by Yu-Hui Chen, et al. ∙ 0 ∙ share

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Coercive Region-level Registration for Multi-modal Images

We propose a coercive approach to simultaneously register and segment mu...
02/26/2015 ∙ by Yu-Hui Chen, et al. ∙ 0 ∙ share

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Parameter estimation in spherical symmetry groups

This paper considers statistical estimation problems where the probabili...
11/10/2014 ∙ by Yu-Hui Chen, et al. ∙ 0 ∙ share

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Marginal Likelihoods for Distributed Parameter Estimation of Gaussian Graphical Models

We consider distributed estimation of the inverse covariance matrix, als...
03/19/2013 ∙ by Zhaoshi Meng, et al. ∙ 0 ∙ share

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Dennis Wei

Featured Co-authors

Pin-Yu Chen

Sijia Liu

David Sontag

Kush R. Varshney

Alfred O. Hero III

Karthikeyan Shanmugam

Alfred O. Hero

Karthikeyan Natesan Ramamurthy

Yue Yu

Prasanna Sattigeri

Alfred Hero

Treatment Effect Estimation using Invariant Risk Minimization

Optimal Policies for the Homogeneous Selective Labels Problem

DAGs with No Fears: A Closer Look at Continuous Optimization for Learning Bayesian Networks

Deciding Fast and Slow: The Role of Cognitive Biases in AI-assisted Decision-making

Consumer-Driven Explanations for Machine Learning Decisions: An Empirical Study of Robustness

An Information-Theoretic Perspective on the Relationship Between Fairness and Accuracy

One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques

Characterization of Overlap in Observational Studies

Teaching AI to Explain its Decisions Using Embeddings and Multi-Task Learning

Generalized Linear Rule Models

Optimized Score Transformation for Fair Classification

Interpretable Subgroup Discovery in Treatment Effect Estimation with Application to Opioid Prescribing Guidelines

TED: Teaching AI to Explain its Decisions

Teaching Meaningful Explanations

Boolean Decision Rules via Column Generation

On the Supermodularity of Active Graph-based Semi-supervised Learning with Stieltjes Matrix Regularization

Distribution-Preserving k-Anonymity

An End-To-End Machine Learning Pipeline That Ensures Fairness Policies

Optimized Data Pre-Processing for Discrimination Prevention

Interpretable Two-level Boolean Rule Learning for Classification

Statistical Estimation and Clustering of Group-invariant Orientation Parameters

A Dictionary Approach to EBSD Indexing

Coercive Region-level Registration for Multi-modal Images

Parameter estimation in spherical symmetry groups

Marginal Likelihoods for Distributed Parameter Estimation of Gaussian Graphical Models