Publications & Preprints

My work centers on two intertwined lines: optimal multiple testing and federated, communication-constrained learning, together with their growing intersection. Author order is by contribution, and all papers are joint with my advisor, Xiaoming Huo, except where noted. See Google Scholar or my CV for the latest.

Optimal Multiple Testing

  1. BOOST: Power-Optimal Strong-FWER Testing for Block-Structured Multiplicity

    Prasanjit Dubey and Xiaoming Huo

    Accepted NeurIPS · 2026 Paper

  2. Most Powerful Tests with Exact Family-Wise Error Rate Control: Necessary Conditions and Fast Computation for General K

    Prasanjit Dubey and Xiaoming Huo

    Revision under review Journal of Computational and Graphical Statistics (JCGS) · 2025 Paper

    An efficient algorithm for the most powerful test under exact FWER control for an arbitrary number of exchangeable hypotheses. The power advantage over Bonferroni, Holm, and Hommel grows with the number of hypotheses (an 84% relative gain at K = 12 in the truncated-normal model).

    ★ Invited talk, Optimisation for Statistics and Learning session, INFORMS Annual Meeting, 2025

  3. Most Powerful Test with Exact Family-Wise Error Rate Control: Necessary Conditions and a Path to Fast Computing

    Prasanjit Dubey and Xiaoming Huo

    PreprintPaper

Federated & Communication-Constrained Learning

  1. Byzantine-Robust Federated RAG via Aligned Calibration and Fixed-Membership Conformal Prediction

    Prasanjit Dubey, Aritra Guha, and Xiaoming Huo

    Under review ICLR · 2027 Paper

  2. Matching Rates and Optimal Allocation for Federated Probe-Logit Distillation under Heterogeneous Bandwidth Budgets

    Prasanjit Dubey and Xiaoming Huo

    Under review Invited submission, Journal of the American Statistical Association (Theory & Methods) · 2026 Paper

    ★ Poster and Travel Award, Conference on Statistical Foundations of AI (CSFAI), University of California, Santa Barbara, 2026

    ★ Poster presented at STAI-X, Harvard University, 2026

  3. Certified Adaptive Refresh: Anytime-Valid Monitoring for Federated Conformal RAG

    Prasanjit Dubey and Xiaoming Huo

    Under review ICLR · 2027 Paper

    ★ Accepted at the NeurIPS 2026 Second Workshop on ML×OR: Mathematical Foundations and Operational Integration of Machine Learning for Uncertainty-Aware Decision-Making

  4. Federated Language Models Under Bandwidth Budgets: Distillation Rates and Conformal Coverage

    Prasanjit Dubey and Xiaoming Huo

    Under review Transactions on Machine Learning Research (TMLR) · 2026 Paper

Federated and Anytime-Valid Multiple Testing

  1. Pooling Sequential Evidence Across Hypotheses: Rate-Optimal Multiple Testing at a Fixed Horizon

    Prasanjit Dubey and Xiaoming Huo

    Under review Electronic Journal of Statistics · 2026 Paper

  2. Federated Multiple Testing with Quantized p-Values under Family-Wise Error Control

    Prasanjit Dubey and Xiaoming Huo

    Under review Bernoulli · 2026 Paper

    ★ Accepted at the NeurIPS 2026 Second Workshop on ML×OR: Mathematical Foundations and Operational Integration of Machine Learning for Uncertainty-Aware Decision-Making

Statistical Machine Learning

  1. LLmFPCA-detect: LLM-powered Multivariate Functional PCA for Anomaly Detection in Sparse Longitudinal Texts

    Prasanjit Dubey, Aritra Guha, Zhengyi Zhou, Qiong Wu, Xiaoming Huo, and Paromita Dubey

    Preprint arXiv · 2025 Paper

    ★ 2nd Place Paper Award, Student Research Showcase, INFORMS Regional Analytics Conference (IRAC), 2025

  2. Generalization Bounds for Magnitude-Based Pruning via Sparse Matrix Sketching

    Etash Kumar Guha, Prasanjit Dubey, and Xiaoming Huo

    Published ICLR 2024 Workshop on Bridging the Gap Between Practice and Theory in Deep Learning Paper