b'Dennis Wei'

research

∙ 06/10/2023

Interpretable Differencing of Machine Learning Models

Understanding the differences between machine learning (ML) models is of...

0 Swagatam Haldar, et al. ∙

research

∙ 04/26/2023

A Statistical Interpretation of the Maximum Subarray Problem

Maximum subarray is a classical problem in computer science that given a...

0 Dennis Wei, et al. ∙

research

∙ 03/03/2023

Convex Bounds on the Softmax Function with Applications to Robustness Verification

The softmax function is a ubiquitous component at the output of neural n...

0 Dennis Wei, et al. ∙

research

∙ 01/15/2023

Who Should Predict? Exact Algorithms For Learning to Defer to Humans

Automated AI classifiers should be able to defer the prediction to a hum...

0 Hussein Mozannar, et al. ∙

research

∙ 11/02/2022

On the Safety of Interpretable Machine Learning: A Maximum Deviation Approach

Interpretable and explainable machine learning has seen a recent surge o...

0 Dennis Wei, et al. ∙

research

∙ 03/09/2022

Downstream Fairness Caveats with Synthetic Healthcare Data

This paper evaluates synthetically generated healthcare data for biases ...

0 Karan Bhanot, et al. ∙

research

∙ 02/02/2022

Analogies and Feature Attributions for Model Agnostic Explanation of Similarity Learners

Post-hoc explanations for black box models have been studied extensively...

0 Karthikeyan Natesan Ramamurthy, et al. ∙

research

∙ 01/04/2022

FROTE: Feedback Rule-Driven Oversampling for Editing Models

Machine learning models may involve decision boundaries that change over...

9 Oznur Alkan, et al. ∙

research

∙ 12/07/2021

Ground-Truth, Whose Truth? – Examining the Challenges with Annotating Toxic Text Datasets

The use of machine learning (ML)-based language models (LMs) to monitor ...

5 Kofi Arhin, et al. ∙

research

∙ 11/16/2021

Interpretable and Fair Boolean Rule Sets via Column Generation

This paper considers the learning of Boolean rules in either disjunctive...

0 Connor Lawless, et al. ∙

research

∙ 09/24/2021

AI Explainability 360: Impact and Design

As artificial intelligence and machine learning algorithms become increa...

46 Vijay Arya, et al. ∙

research

∙ 08/03/2021

Your fairness may vary: Group fairness of pretrained language models in toxic text classification

We study the performance-fairness trade-off in more than a dozen fine-tu...

0 Ioana Baldini, et al. ∙

research

∙ 03/13/2021

Treatment Effect Estimation using Invariant Risk Minimization

Inferring causal individual treatment effect (ITE) from observational da...

19 Abhin Shah, et al. ∙

research

∙ 11/02/2020

Optimal Policies for the Homogeneous Selective Labels Problem

Selective labels are a common feature of consequential decision-making a...

0 Dennis Wei, et al. ∙

research

∙ 10/18/2020

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

This paper re-examines a continuous optimization framework dubbed NOTEAR...

0 Dennis Wei, et al. ∙

research

∙ 10/15/2020

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...

0 Charvi Rastogi, et al. ∙

research

∙ 01/13/2020

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

Many proposed methods for explaining machine learning predictions are in...

0 Michael Hind, et al. ∙

research

∙ 10/17/2019

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...

29 Sanghamitra Dutta, et al. ∙

research

∙ 09/06/2019

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

As artificial intelligence and machine learning algorithms make further ...

36 Vijay Arya, et al. ∙

research

∙ 07/09/2019

Characterization of Overlap in Observational Studies

Overlap between treatment groups is required for nonparametric estimatio...

5 Fredrik D. Johansson, et al. ∙

research

∙ 06/05/2019

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

Using machine learning in high-stakes applications often requires predic...

4 Noel C. F. Codella, et al. ∙

research

∙ 06/05/2019

Generalized Linear Rule Models

This paper considers generalized linear models using rule-based features...

0 Dennis Wei, et al. ∙

research

∙ 05/31/2019

Optimized Score Transformation for Fair Classification

This paper considers fair probabilistic classification where the outputs...

0 Dennis Wei, et al. ∙

research

∙ 05/08/2019

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...

11 Chirag Nagpal, et al. ∙

research

∙ 11/12/2018

TED: Teaching AI to Explain its Decisions

Artificial intelligence systems are being increasingly deployed due to t...

12 Noel C. F. Codella, et al. ∙

research

∙ 05/29/2018

Teaching Meaningful Explanations

The adoption of machine learning in high-stakes applications such as hea...

0 Noel C. F. Codella, et al. ∙

research

∙ 05/24/2018

Boolean Decision Rules via Column Generation

This paper considers the learning of Boolean rules in either disjunctive...

0 Sanjeeb Dash, et al. ∙

research

∙ 04/09/2018

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...

0 Pin-Yu Chen, et al. ∙

research

∙ 11/05/2017

Distribution-Preserving k-Anonymity

Preserving the privacy of individuals by protecting their sensitive attr...

0 Dennis Wei, et al. ∙

research

∙ 10/18/2017

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

In consequential real-world applications, machine learning (ML) based sy...

0 Samiulla Shaikh, et al. ∙

research

∙ 04/11/2017

Optimized Data Pre-Processing for Discrimination Prevention

Non-discrimination is a recognized objective in algorithmic decision mak...

0 Flavio P. Calmon, et al. ∙

research

∙ 11/23/2015

Interpretable Two-level Boolean Rule Learning for Classification

This paper proposes algorithms for learning two-level Boolean rules in C...

0 Guolong Su, et al. ∙

research

∙ 03/15/2015

Statistical Estimation and Clustering of Group-invariant Orientation Parameters

We treat the problem of estimation of orientation parameters whose value...

0 Yu-Hui Chen, et al. ∙

research

∙ 02/26/2015

A Dictionary Approach to EBSD Indexing

We propose a framework for indexing of grain and sub-grain structures in...

0 Yu-Hui Chen, et al. ∙

research

∙ 02/26/2015

Coercive Region-level Registration for Multi-modal Images

We propose a coercive approach to simultaneously register and segment mu...

0 Yu-Hui Chen, et al. ∙

research

∙ 11/10/2014

Parameter estimation in spherical symmetry groups

This paper considers statistical estimation problems where the probabili...

0 Yu-Hui Chen, et al. ∙

research

∙ 03/19/2013

Marginal Likelihoods for Distributed Parameter Estimation of Gaussian Graphical Models

We consider distributed estimation of the inverse covariance matrix, als...

0 Zhaoshi Meng, et al. ∙

Dennis Wei

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