1 citations · 1 across the 6 of their papers we have counts for
6 papers
Imperfect Influence, Preserved Rankings: A Theory of TRAK for Data Attribution
Han Tong, Shubhangi Ghosh, Haolin Zou +1
Data attribution, tracing a model's prediction back to specific training data, is an important tool for interpreting sophisticated AI models. The widely used TRAK algorithm address…
Gaussian Certified Unlearning in High Dimensions: A Hypothesis Testing Approach
Aaradhya Pandey, Arnab Auddy, Haolin Zou +2
Machine unlearning seeks to efficiently remove the influence of selected data while preserving generalization. Significant progress has been made in low dimensions , but…
Newfluence: Boosting Model interpretability and Understanding in High Dimensions
Haolin Zou, Arnab Auddy, Yongchan Kwon +2
The increasing complexity of machine learning (ML) and artificial intelligence (AI) models has created a pressing need for tools that help scientists, engineers, and policymakers i…
Certified Data Removal Under High-dimensional Settings
Haolin Zou, Arnab Auddy, Yongchan Kwon +2
Machine unlearning focuses on the computationally efficient removal of specific training data from trained models, ensuring that the influence of forgotten data is effectively elim…
Theoretical Analysis of Leave-one-out Cross Validation for Non-differentiable Penalties under High-dimensional Settings
Haolin Zou, Arnab Auddy, Kamiar Rahnama Rad +1
Despite a large and significant body of recent work focused on estimating the out-of-sample risk of regularized models in the high dimensional regime, a theoretical understanding o…
Approximate Leave-one-out Cross Validation for Regression with Regularizers (extended version)
Arnab Auddy, Haolin Zou, Kamiar Rahnama Rad +1
The out-of-sample error (OO) is the main quantity of interest in risk estimation and model selection. Leave-one-out cross validation (LO) offers a (nearly) distribution-free yet co…