8 papers
Personalized Federated Learning via Variance-Aware Nonparametric Empirical Bayes
Jae Ho Chang, Arnab Auddy, Subhadeep Paul
We develop a new approach to Personalized Federated Learning across heterogeneous clients using Nonparametric Empirical Bayes (NPEB). Leveraging the asymptotic normality of local p…
Differentially Private Nonparametric Modal Learning with Applications to Regression and Clustering
Arkajyoti Bhattacharjee, Arnab Auddy
Density modes provide a localized and interpretable summary of multimodal distributions, but their estimation under rigorous differential privacy constraints remains largely unexpl…
Statistical Limits and Efficient Algorithms for Differentially Private Federated Learning
Arnab Auddy, Xiangni Peng, Subhadeep Paul
Federated Learning is a leading framework for training ML and AI models collaboratively across numerous user devices or databases. We study the trade-offs among estimation accuracy…
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…
On Spectral Learning for Odeco Tensors: Perturbation, Initialization, and Algorithms
Arnab Auddy, Ming Yuan
We study spectral learning for orthogonally decomposable (odeco) tensors, emphasizing the interplay between statistical limits, optimization geometry, and initialization. Unlike ma…
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…