Inject Machine Learning into Significance Test for Misspecified Linear Models. - Jiaye Teng; Yang Yuan (CoRR, 2020)

T-SCI: A Two-Stage Conformal Inference Algorithm with Guaranteed Coverage for Cox-MLP. - Jiaye Teng; Zeren Tan; Yang Yuan (ICML, 2021)

Can Pretext-Based Self-Supervised Learning Be Boosted by Downstream Data? A Theoretical Analysis. - Jiaye Teng; Weiran Huang 001 (CoRR, 2021)

Can Pretext-Based Self-Supervised Learning Be Boosted by Downstream Data? A Theoretical Analysis. - Jiaye Teng; Weiran Huang 001; Haowei He (AISTATS, 2022)

Towards Understanding Generalization via Decomposing Excess Risk Dynamics. - Jiaye Teng; Jianhao Ma; Yang Yuan (ICLR, 2022)

Fighting Fire with Fire: Avoiding DNN Shortcuts through Priming. - Chuan Wen; Jianing Qian; Jierui Lin; Jiaye Teng; Dinesh Jayaraman; Yang Gao 029 (ICML, 2022)

Relaxing the Feature Covariance Assumption: Time-Variant Bounds for Benign Overfitting in Linear Regression. - Jing Xu; Jiaye Teng; Andrew Chi-Chih Yao (CoRR, 2022)

Realistic Deep Learning May Not Fit Benignly. - Kaiyue Wen; Jiaye Teng; Jingzhao Zhang (CoRR, 2022)

Anomaly Detection with Test Time Augmentation and Consistency Evaluation. - Haowei He; Jiaye Teng; Yang Yuan (CoRR, 2022)

Predictive Inference with Feature Conformal Prediction. - Jiaye Teng; Chuan Wen; Dinghuai Zhang; Yoshua Bengio; Yang Gao 029; Yang Yuan (ICLR, 2023)

Benign Overfitting in Classification: Provably Counter Label Noise with Larger Models. - Kaiyue Wen; Jiaye Teng; Jingzhao Zhang (ICLR, 2023)

On Uni-Modal Feature Learning in Supervised Multi-Modal Learning. - Chenzhuang Du; Jiaye Teng; Tingle Li; Yichen Liu; Tianyuan Yuan; Yue Wang; Yang Yuan; Hang Zhao (ICML, 2023)

Finding Generalization Measures by Contrasting Signal and Noise. - Jiaye Teng; Bohang Zhang; Ruichen Li; Haowei He; Yequan Wang; Yan Tian; Yang Yuan (ICML, 2023)

Towards Data-Algorithm Dependent Generalization: a Case Study on Overparameterized Linear Regression. - Jing Xu; Jiaye Teng; Yang Yuan; Andrew C. Yao (NeurIPS, 2023)

Lower Generalization Bounds for GD and SGD in Smooth Stochastic Convex Optimization. - Peiyuan Zhang; Jiaye Teng; Jingzhao Zhang (CoRR, 2023)

High-resolution probabilistic load forecasting: A learning ensemble approach. - Chenbei Lu; Jinhao Liang; Wenqian Jiang; Jiaye Teng; Chenye Wu (J. Frankl. Inst., 2023)

Predictive Inference With Fast Feature Conformal Prediction. - Zihao Tang 001; Boyuan Wang; Chuan Wen; Jiaye Teng (CoRR, 2024)

Generalization Lower Bounds for GD and SGD in Smooth Stochastic Convex Optimization. - Peiyuan Zhang; Jiaye Teng; Jingzhao Zhang (AISTATS, 2025)

Minimax Optimal Two-Stage Algorithm For Moment Estimation Under Covariate Shift. - Zhen Zhang; Xin Liu; Shaoli Wang; Jiaye Teng (ICLR, 2025)

Disentangling Feature Structure: A Mathematically Provable Two-Stage Training Dynamics in Transformers. - Zixuan Gong; Jiaye Teng; Yong Liu (CoRR, 2025)

Theoretical Modeling of LLM Self-Improvement Training Dynamics Through Solver-Verifier Gap. - Yifan Sun; Yushan Liang; Zhen Zhang; Jiaye Teng (CoRR, 2025)