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A list of all the posts and pages found on the site. For you robots out there, there is an XML version available for digesting as well.
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About me
About Minxing Zheng
Research
Research in uncertainty quantification, conformal prediction, reliable AI, and scientific machine learning.
Posts
publications
Interactive Visualization and Representation Analysis Applied to Glacier Segmentation
Published in ISPRS International Journal of Geo-Information, 2022
Interactive visualization and representation analysis for understanding glacier segmentation models, diagnosing data issues, and guiding model improvement.
Recommended citation: Minxing Zheng, Xinran Miao, and Kris Sankaran. (2022). "Interactive Visualization and Representation Analysis Applied to Glacier Segmentation." ISPRS International Journal of Geo-Information, 11(8), 415.
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Change Point Inference for Non-Euclidean Data Sequences using Distance Profiles
Published in arXiv preprint; revised 2026, 2023
A tuning-parameter-free, fully nonparametric framework for detecting and locating distributional changes in metric-space data using distance profiles.
Recommended citation: Paromita Dubey and Minxing Zheng. (2023; revised 2026). "Change Point Inference for Non-Euclidean Data Sequences using Distance Profiles." arXiv:2311.16025.
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Generative Conformal Prediction with Vectorized Non-Conformity Scores
Published in arXiv preprint, 2024
A generative conformal framework that constructs adaptive, efficient prediction regions for complex multidimensional outcomes.
Recommended citation: Minxing Zheng and Shixiang Zhu. (2024). "Generative Conformal Prediction with Vectorized Non-Conformity Scores." arXiv:2410.13735.
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MISLEADER: Defending against Model Extraction with Ensembles of Distilled Models
Published in arXiv preprint, 2025
A defense against model extraction that preserves benign utility without relying on out-of-distribution query assumptions.
Recommended citation: Xueqi Cheng, Minxing Zheng, Shixiang Zhu, and Yushun Dong. (2025). "MISLEADER: Defending against Model Extraction with Ensembles of Distilled Models." arXiv:2506.02362.
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Learning to Test: Physics-Informed Representation for Dynamical Instability Detection
Published in arXiv preprint, 2026
A physics-informed, test-oriented learning framework for detecting dynamical instability under distribution shift with controlled Type I error.
Recommended citation: Minxing Zheng, Zewei Deng, Liyan Xie, and Shixiang Zhu. (2026). "Learning to Test: Physics-Informed Representation for Dynamical Instability Detection." arXiv:2604.10967.
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Beyond Predicting Responses: Conformal Inference for Latent Distributional Parameters
Published in arXiv preprint, 2026
A prior-free conformal framework for constructing finite-sample valid uncertainty sets for latent distributional parameters using only observed context–response pairs and a specified forward model.
Recommended citation: Minxing Zheng, Wenbin Zhou, and Shixiang Zhu. (2026). "Beyond Predicting Responses: Conformal Inference for Latent Distributional Parameters." arXiv:2608.03607.
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