7.6k citations · 9.7k across the 31 of their papers we have counts for
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cs.LG2023★ 2 cited
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…
cs.LG2023
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…
cs.LG2023★ 6 cited
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…