284 citations · 734 across the 15 of their papers we have counts for
15 papers
Stochastic Amortization: A Unified Approach to Accelerate Feature and Data Attribution
Ian Covert, Chanwoo Kim, Su-In Lee +2
Many tasks in explainable machine learning, such as data valuation and feature attribution, perform expensive computation for each data point and are intractable for large datasets…
Feature Selection in the Contrastive Analysis Setting
Ethan Weinberger, Ian Covert, Su-In Lee
Contrastive analysis (CA) refers to the exploration of variations uniquely enriched in a target dataset as compared to a corresponding background dataset generated from sources of…
On the Robustness of Removal-Based Feature Attributions
Chris Lin, Ian Covert, Su-In Lee
To explain predictions made by complex machine learning models, many feature attribution methods have been developed that assign importance scores to input features. Some recent wo…
Estimating Conditional Mutual Information for Dynamic Feature Selection
Soham Gadgil, Ian Covert, Su-In Lee
Dynamic feature selection, where we sequentially query features to make accurate predictions with a minimal budget, is a promising paradigm to reduce feature acquisition costs and…
Learning to Maximize Mutual Information for Dynamic Feature Selection
Ian Covert, Wei Qiu, Mingyu Lu +3
Feature selection helps reduce data acquisition costs in ML, but the standard approach is to train models with static feature subsets. Here, we consider the dynamic feature selecti…
Algorithms to estimate Shapley value feature attributions
Hugh Chen, Ian C. Covert, Scott M. Lundberg +1
Feature attributions based on the Shapley value are popular for explaining machine learning models; however, their estimation is complex from both a theoretical and computational s…