1 citations · 2 across the 9 of their papers we have counts for
10 papers
Transfer Calibrated Prediction Powered Inference
Aditya T. Vadlamani, Jae Ho Chang, Srinivasan Parthasarathy +1
Prediction-powered inference (PPI) and its power-tuned extension (PPI++) improve confidence intervals by combining a small gold-standard labeled sample with a large AI model's pred…
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 Prediction, Peer Effect Estimation, and Prediction-Powered Inference with LLM Text Measures
Shanjukta Nath, Jiwon Hong, Jae Ho Chang +2
We provide a new framework for estimating peer effects when outcomes are multivariate behavioral measures derived from written text using an LLM and the network formation is endoge…
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…
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…
The co-varying ties between networks and item responses via latent variables
Selena Wang, Plamena Powla, Tracy Sweet +1
Relationships among teachers are known to influence their teaching-related perceptions. We study whether and how teachers' advising relationships (networks) are related to their pe…
Embedding Network Autoregression for time series analysis and causal peer effect inference
Jae Ho Chang, Subhadeep Paul
We propose an Embedding Network Autoregressive Model for multivariate networked longitudinal data. We assume the network is generated from a latent variable model, and these unobse…
Spatial autoregressive model with measurement error in covariates
Subhadeep Paul, Shanjukta Nath
The Spatial AutoRegressive model (SAR) is commonly used in studies involving spatial and network data to estimate the spatial or network peer influence and the effects of covariate…