5 papers
Statistical Unlearning of Distributions: A Hypothesis Testing Approach
Aaradhya Pandey, Sanjeev Kulkarni
Machine learning systems increasingly face requirements to forget not only individual data points, but entire domains of information, such as toxic language, copyrighted corpora, o…
Infinitely divisible privacy and beyond I: resolution of the conjecture
Aaradhya Pandey, Arian Maleki, Sanjeev Kulkarni
Differential privacy is increasingly formalized through the lens of hypothesis testing via the robust and interpretable -DP framework, where privacy guarantees are encoded by a…
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…
Exact recovery in Gaussian weighted stochastic block model and planted dense subgraphs: Statistical and algorithmic thresholds
Aaradhya Pandey, Sanjeev Kulkarni
In this paper, we study the exact recovery problem in the Gaussian weighted version of the Stochastic block model with two symmetric communities. We provide the information-theoret…
Community detection in the hypergraph stochastic block model and reconstruction on hypertrees
Yuzhou Gu, Aaradhya Pandey
We study the weak recovery problem on the -uniform hypergraph stochastic block model (-HSBM) with two balanced communities. In this model, vertices are randomly divided i…