activity
20242026
collaborators

12 papers

stat.ME2026

Conformal Novelty Detection via Data-enhanced Localization

Xiaoyang Wu, Jiajun Liu, Haojie Ren +1

Novelty detection is an important area of research in both statistics and machine learning. In this paper, we focus on the conditional novelty detection problem, where novelties ar…

stat.ME2026

Controlling the False Discovery Rate via Model-Agnostic Importance Measures

Mengtao Wen, Haojie Ren, Changliang Zou

Selecting covariates while controlling the false discovery rate (FDR) is critical for interpretable scientific discovery, especially with complex machine learning models. Tradition…

stat.ML2026

Minimum Volume Optimal Transport for Multivariate Conformal Prediction

Chuchen Zhang, Haojie Ren, Zhaojun Wang +2

Optimal transport (OT) enables multivariate conformal prediction (CP) to construct flexible prediction sets that reflect the underlying complex distributional geometry while retain…

stat.ME2026

Shape-Adaptive Conditional Calibration for Conformal Prediction via Minimax Optimization

Yajie Bao, Chuchen Zhang, Zhaojun Wang +2

Achieving valid conditional coverage in conformal prediction is challenging due to the theoretical difficulty of satisfying pointwise constraints in finite samples. Building upon t…

stat.ME2025

Aggregating Conformal Prediction Sets via α-Allocation

Congbin Xu, Yue Yu, Haojie Ren +2

Conformal prediction offers a distribution-free framework for constructing prediction sets with finite-sample coverage. Yet, efficiently leveraging multiple conformity scores to re…

stat.ME2025

Feedback-Enhanced Online Multiple Testing with Applications to Conformal Selection

Lin Lu, Yuyang Huo, Haojie Ren +2

This work studies online multiple testing with feedback, where decisions are made sequentially, and the true state of the hypothesis is revealed after decisions are made, either in…

stat.ME2025

Unified Conformalized Multiple Testing with Full Data Efficiency

Yuyang Huo, Xiaoyang Wu, Changliang Zou +1

Conformalized multiple testing offers a model-free way to control predictive uncertainty in decision-making. Existing methods typically use only part of the available data to build…

stat.ML2025

Optimal Model Selection for Conformalized Robust Optimization

Yajie Bao, Yang Hu, Haojie Ren +2

In decision-making under uncertainty, Contextual Robust Optimization (CRO) provides reliability by minimizing the worst-case decision loss over a prediction set. While recent advan…

stat.ME2025

e-GAI: e-value-based Generalized -Investing for Online False Discovery Rate Control

Yifan Zhang, Zijian Wei, Haojie Ren +1

Online multiple hypothesis testing has attracted a lot of attention in many applications, e.g., anomaly status detection and stock market price monitoring. The state-of-the-art gen…

stat.ML2025

Conformal Prediction with Cellwise Outliers: A Detect-then-Impute Approach

Qian Peng, Yajie Bao, Haojie Ren +2

Conformal prediction is a powerful tool for constructing prediction intervals for black-box models, providing a finite sample coverage guarantee for exchangeable data. However, thi…

stat.ML2025

Error-quantified Conformal Inference for Time Series

Junxi Wu, Dongjian Hu, Yajie Bao +2

Uncertainty quantification in time series prediction is challenging due to the temporal dependence and distribution shift on sequential data. Conformal inference provides a pivotal…

stat.ML2024

CAP: A General Algorithm for Online Selective Conformal Prediction with FCR Control

Yajie Bao, Yuyang Huo, Haojie Ren +1

We study the problem of post-selection predictive inference in an online fashion. To avoid devoting resources to unimportant units, a preliminary selection of the current individua…