5 papers
Online Localized Conformal Prediction
Yuheng Lai, Garvesh Raskutti
Conformal prediction is a framework that provides valid uncertainty quantification for general models with exchangeable data. However, in the online learning and time-series settin…
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…