270 citations · 637 across the 34 of their papers we have counts for
35 papers
CoFrNets: Interpretable Neural Architecture Inspired by Continued Fractions
Isha Puri, Amit Dhurandhar, Tejaswini Pedapati +3
In recent years there has been a considerable amount of research on local post hoc explanations for neural networks. However, work on building interpretable neural architectures ha…
Interpretable Differencing of Machine Learning Models
Swagatam Haldar, Diptikalyan Saha, Dennis Wei +2
Understanding the differences between machine learning (ML) models is of interest in scenarios ranging from choosing amongst a set of competing models, to updating a deployed model…
Who Should Predict? Exact Algorithms For Learning to Defer to Humans
Hussein Mozannar, Hunter Lang, Dennis Wei +3
Automated AI classifiers should be able to defer the prediction to a human decision maker to ensure more accurate predictions. In this work, we jointly train a classifier with a re…
On the Safety of Interpretable Machine Learning: A Maximum Deviation Approach
Dennis Wei, Rahul Nair, Amit Dhurandhar +3
Interpretable and explainable machine learning has seen a recent surge of interest. We focus on safety as a key motivation behind the surge and make the relationship between interp…
Downstream Fairness Caveats with Synthetic Healthcare Data
Karan Bhanot, Ioana Baldini, Dennis Wei +2
This paper evaluates synthetically generated healthcare data for biases and investigates the effect of fairness mitigation techniques on utility-fairness. Privacy laws limit access…
Analogies and Feature Attributions for Model Agnostic Explanation of Similarity Learners
Karthikeyan Natesan Ramamurthy, Amit Dhurandhar, Dennis Wei +1
Post-hoc explanations for black box models have been studied extensively in classification and regression settings. However, explanations for models that output similarity between…