171 citations · 624 across the 37 of their papers we have counts for
26 papers · 1 filter
Null importance: Disentangling relevance for interpretable machine learning
Garvesh Raskutti, Kris Sankaran, Jiaxin Ye
Feature importance is central to interpretable machine learning, but the term "importance" encompasses several fundamentally different notions of relevance. We develop a unified pe…
MinShap: A Shapley-Based Framework for Feature Redundancy
Chenghui Zheng, Garvesh Raskutti
Shapley values provide a flexible framework for attributing feature contributions to model predictions, but they are not naturally suited for feature selection: a feature may recei…
A Theoretical Framework for LLM Fine-tuning Using Early Stopping for Non-random Initialization
Zexuan Sun, Garvesh Raskutti
In the era of large language models (LLMs), fine-tuning pretrained models has become ubiquitous. Yet the theoretical underpinning remains an open question. A central question is wh…
Comparing Model-agnostic Feature Selection Methods through Relative Efficiency
Chenghui Zheng, Garvesh Raskutti
Feature selection and importance estimation in a model-agnostic setting is an ongoing challenge of significant interest. Wrapper methods are commonly used because they are typicall…
Reliable and scalable variable importance estimation via warm-start and early stopping
Zexuan Sun, Garvesh Raskutti
As opaque black-box predictive models become more prevalent, the need to develop interpretations for these models is of great interest. The concept of variable importance and Shapl…
Fast, Distribution-free Predictive Inference for Neural Networks with Coverage Guarantees
Yue Gao, Garvesh Raskutti, Rebecca Willet
This paper introduces a novel, computationally-efficient algorithm for predictive inference (PI) that requires no distributional assumptions on the data and can be computed faster…