Publications
My research spans conformal inference, reliable machine learning, scientific AI, and interpretable modeling. For citation counts and the latest updates, visit Google Scholar or arXiv.
Preprints & Working Papers
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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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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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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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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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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Peer-Reviewed Publications
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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