research
∙
06/10/2023
Interpretable Differencing of Machine Learning Models
Understanding the differences between machine learning (ML) models is of...
share
research
∙
04/26/2023
A Statistical Interpretation of the Maximum Subarray Problem
Maximum subarray is a classical problem in computer science that given a...
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...
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...
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...
research
∙
03/09/2022
Downstream Fairness Caveats with Synthetic Healthcare Data
This paper evaluates synthetically generated healthcare data for biases ...
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...
research
∙
01/04/2022
FROTE: Feedback Rule-Driven Oversampling for Editing Models
Machine learning models may involve decision boundaries that change over...
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 ...
research
∙
11/16/2021
Interpretable and Fair Boolean Rule Sets via Column Generation
This paper considers the learning of Boolean rules in either disjunctive...
research
∙
09/24/2021
AI Explainability 360: Impact and Design
As artificial intelligence and machine learning algorithms become increa...
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...
research
∙
03/13/2021
Treatment Effect Estimation using Invariant Risk Minimization
Inferring causal individual treatment effect (ITE) from observational da...
research
∙
11/02/2020
Optimal Policies for the Homogeneous Selective Labels Problem
Selective labels are a common feature of consequential decision-making a...
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...
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...
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...
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...
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 ...
research
∙
07/09/2019
Characterization of Overlap in Observational Studies
Overlap between treatment groups is required for nonparametric estimatio...
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...
research
∙
06/05/2019
Generalized Linear Rule Models
This paper considers generalized linear models using rule-based features...
research
∙
05/31/2019
Optimized Score Transformation for Fair Classification
This paper considers fair probabilistic classification where the outputs...
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...
research
∙
11/12/2018
TED: Teaching AI to Explain its Decisions
Artificial intelligence systems are being increasingly deployed due to t...
research
∙
05/29/2018
Teaching Meaningful Explanations
The adoption of machine learning in high-stakes applications such as hea...
research
∙
05/24/2018
Boolean Decision Rules via Column Generation
This paper considers the learning of Boolean rules in either disjunctive...
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...
research
∙
11/05/2017
Distribution-Preserving k-Anonymity
Preserving the privacy of individuals by protecting their sensitive attr...
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...
research
∙
04/11/2017
Optimized Data Pre-Processing for Discrimination Prevention
Non-discrimination is a recognized objective in algorithmic decision mak...
research
∙
11/23/2015
Interpretable Two-level Boolean Rule Learning for Classification
This paper proposes algorithms for learning two-level Boolean rules in C...
research
∙
03/15/2015
Statistical Estimation and Clustering of Group-invariant Orientation Parameters
We treat the problem of estimation of orientation parameters whose value...
research
∙
02/26/2015
A Dictionary Approach to EBSD Indexing
We propose a framework for indexing of grain and sub-grain structures in...
research
∙
02/26/2015
Coercive Region-level Registration for Multi-modal Images
We propose a coercive approach to simultaneously register and segment mu...
research
∙
11/10/2014
Parameter estimation in spherical symmetry groups
This paper considers statistical estimation problems where the probabili...
research
∙
03/19/2013