7 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…
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…
Transfer Learning with Distance Covariance for Random Forest: Error Bounds and an EHR Application
Chenze Li, Subhadeep Paul
We propose a method for transfer learning in nonparametric regression using a random forest (RF) with distance covariance-based feature weights, assuming the unknown source and tar…
Recidivism and Peer Influence with LLM Text Embeddings in Low Security Correctional Facilities
Shanjukta Nath, Jiwon Hong, Jae Ho Chang +2
Studying peer effects in language is critical because they often reflect behavioral and personality traits that are important determinants of economic outcomes. However, language i…
Heterogeneous transfer learning for high-dimensional regression with feature mismatch
Jae Ho Chang, Massimiliano Russo, Subhadeep Paul
We study Heterogeneous Transfer Learning (HTL) for high-dimensional regression with differing feature sets. Such feature mismatch arises when some variables available in a data-ric…
Spectral clustering for dependent community Hawkes process models of temporal networks
Lingfei Zhao, Hadeel Soliman, Kevin S. Xu +1
Temporal networks observed continuously over time through timestamped relational events data are commonly encountered in application settings including online social media communic…