works on

From the 1 of 8 linked papers with an AI index.

activity
20242026
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8 papers

math.ST2026

Incomplete U-Statistics of Equireplicate Designs: Berry-Esseen Bound and Efficient Construction

Cesare Miglioli, Jordan Awan

The paper develops a hypergraph‑based framework for incomplete U‑statistics, proving a Berry‑Esseen bound that allows Gaussian approximations even in degenerate cases, and provides…

econ.TH2026

Public Good Provision under Locally Private Signals

Behrooz Moosavi Ramezanzadeh, Jordan Awan

We study public-good provision when a planner observes agents' preferences only through a fixed local-privacy channel that randomizes each report before it reaches the planner. We…

stat.ML2026

Near-Optimal Private Tests for Simple and MLR Hypotheses

Yu-Wei Chen, Raghu Pasupathy, Jordan Awan

We develop a near-optimal testing procedure under the framework of Gaussian differential privacy for simple as well as one- and two-sided tests under monotone likelihood ratio cond…

stat.ML2026

Beyond Data Splitting: Full-Data Conformal Prediction by Differential Privacy

Young Hyun Cho, Jordan Awan

Privacy protection and uncertainty quantification are increasingly important in data-driven decision making. Conformal prediction provides finite-sample marginal coverage, but exis…

stat.ME2025

Differentially Private Covariate Balancing Causal Inference

Yuki Ohnishi, Jordan Awan

Differential privacy is the leading mathematical framework for privacy protection, providing a probabilistic guarantee that safeguards individuals' private information when publish…

stat.ML2025

Optimal Survey Design for Private Mean Estimation

Yu-Wei Chen, Raghu Pasupathy, Jordan A. Awan

This work identifies the first privacy-aware stratified sampling scheme that minimizes the variance for general private mean estimation under the Laplace, Discrete Laplace (DLap) a…