Yan Sun
Assistant Professor, Mathematical Sciences
212 Cullimore Hall (CULM)
Education
M.Sc.; Purdue University; Joint Statistics and Computing Science; 2022
Ph.D.; Purdue University; Statistics; 2022
B.S.; Zhejiang University; Mathematics and Applied Mathematics; 2017
Ph.D.; Purdue University; Statistics; 2022
B.S.; Zhejiang University; Mathematics and Applied Mathematics; 2017
Website
Past Courses
MATH 644: REGRESSION ANALYSIS METHODS
MATH 678: STATISTICAL METHODS IN DATA SCIENCE
MATH 678: STATISTICAL METHODS IN DATA SCIENCE
Conference Proceeding
"Statistical early stopping for reasoning models"
International Conference on Machine Learning, July (3rd Quarter/Summer), 2026.
"Singleton-Optimized Conformal Prediction"
International Conference on Learning Representations, April (2nd Quarter/Spring), 2026.
"Foundations of top-$ k $ decoding for language models"
Advances in Neural Information Processing Systems, December, 2025.
"Sparse deep learning for time series data: theory and applications"
Advances in Neural Information Processing Systems, 2023.
"Nonlinear sufficient dimension reduction with a stochastic neural network"
Advances in Neural Information Processing Systems, 2022.
"Sparse deep learning: A new framework immune to local traps and miscalibration"
Advances in Neural Information Processing Systems, 2021.
"Variable selection via penalized neural network: a drop-out-one loss approach"
International Conference on Machine Learning, 2018.
International Conference on Machine Learning, July (3rd Quarter/Summer), 2026.
"Singleton-Optimized Conformal Prediction"
International Conference on Learning Representations, April (2nd Quarter/Spring), 2026.
"Foundations of top-$ k $ decoding for language models"
Advances in Neural Information Processing Systems, December, 2025.
"Sparse deep learning for time series data: theory and applications"
Advances in Neural Information Processing Systems, 2023.
"Nonlinear sufficient dimension reduction with a stochastic neural network"
Advances in Neural Information Processing Systems, 2022.
"Sparse deep learning: A new framework immune to local traps and miscalibration"
Advances in Neural Information Processing Systems, 2021.
"Variable selection via penalized neural network: a drop-out-one loss approach"
International Conference on Machine Learning, 2018.
Journal Article
Sehwan Kim, Yan Sun, Faming Liang.
2026. "Sublinearly structured deep neural networks achieve feature learning consistency for compositional functions."
Statistical Learning and Data Science , pp. 100007.
Zijun Gao, Han Su, Yan Sun. 2026. "Statistical inference for generative model comparison.." Transaction on Machine Learning Research .
Jialin Mao, Itay Griniasty, Yan Sun, Mark K Transtrum, James P Sethna, Pratik Chaudhari. 2026. "Analytical characterization of sloppiness in neural networks: Insights from linear models." Physical Review E , vol. 113 , no. 1 , pp. 015306.
Yan Sun, Faming Liang. 2025. "Uncertainty Quantification for Large-Scale Deep Networks via Post-StoNet Modeling." Statistica Sinica .
Faming Liang, Sehwan Kim, Yan Sun. 2025. "Extended fiducial inference: toward an automated process of statistical inference." Journal of the Royal Statistical Society Series B: Statistical Methodology , vol. 87 , no. 1 , pp. 98--131.
Zijun Gao, Han Su, Yan Sun. 2026. "Statistical inference for generative model comparison.." Transaction on Machine Learning Research .
Jialin Mao, Itay Griniasty, Yan Sun, Mark K Transtrum, James P Sethna, Pratik Chaudhari. 2026. "Analytical characterization of sloppiness in neural networks: Insights from linear models." Physical Review E , vol. 113 , no. 1 , pp. 015306.
Yan Sun, Faming Liang. 2025. "Uncertainty Quantification for Large-Scale Deep Networks via Post-StoNet Modeling." Statistica Sinica .
Faming Liang, Sehwan Kim, Yan Sun. 2025. "Extended fiducial inference: toward an automated process of statistical inference." Journal of the Royal Statistical Society Series B: Statistical Methodology , vol. 87 , no. 1 , pp. 98--131.
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