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20162025
most citedGeneralized Linear Rule Models

16 citations · 44 across the 12 of their papers we have counts for

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Showing 2019Show all

6 papers · 1 filter

stat.ML2019

Is There a Trade-Off Between Fairness and Accuracy? A Perspective Using Mismatched Hypothesis Testing

Sanghamitra Dutta, Dennis Wei, Hazar Yueksel +3

A trade-off between accuracy and fairness is almost taken as a given in the existing literature on fairness in machine learning. Yet, it is not preordained that accuracy should dec…

cs.AI2019

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

Vijay Arya, Rachel K. E. Bellamy, Pin-Yu Chen +17

As artificial intelligence and machine learning algorithms make further inroads into society, calls are increasing from multiple stakeholders for these algorithms to explain their…

cs.LG2019

Characterization of Overlap in Observational Studies

Michael Oberst, Fredrik D. Johansson, Dennis Wei +4

Overlap between treatment groups is required for non-parametric estimation of causal effects. If a subgroup of subjects always receives the same intervention, we cannot estimate th…

cs.LG20192 cited

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

Noel C. F. Codella, Michael Hind, Karthikeyan Natesan Ramamurthy +5

Using machine learning in high-stakes applications often requires predictions to be accompanied by explanations comprehensible to the domain user, who has ultimate responsibility f…

cs.LG201916 cited

Generalized Linear Rule Models

Dennis Wei, Sanjeeb Dash, Tian Gao +1

This paper considers generalized linear models using rule-based features, also referred to as rule ensembles, for regression and probabilistic classification. Rules facilitate mode…

cs.LG2019

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

Chirag Nagpal, Dennis Wei, Bhanukiran Vinzamuri +4

The dearth of prescribing guidelines for physicians is one key driver of the current opioid epidemic in the United States. In this work, we analyze medical and pharmaceutical claim…