A Geodesic-Based Riemannian Gradient Approach to Averaging on the Lorentz Group. - Jing Wang; Huafei Sun; Didong Li (Entropy, 2017)

Classification via local manifold approximation. - Didong Li; David B. Dunson (CoRR, 2019)

Efficient Curvature Estimation for Oriented Point Clouds. - Yueqi Cao; Didong Li; Huafei Sun; Amir H. Assadi; Shiqiang Zhang (CoRR, 2019)

Probabilistic Contrastive Principal Component Analysis. - Didong Li; Andrew Jones; Barbara E. Engelhardt (CoRR, 2020)

A Geometric Approach to Average Problems on Multinomial and Negative Multinomial Models. - Mingming Li; Huafei Sun; Didong Li (Entropy, 2020)

From the Greene-Wu Convolution to Gradient Estimation over Riemannian Manifolds. - Tianyu Wang; Yifeng Huang; Didong Li (CoRR, 2021)

Efficient Weingarten map and curvature estimation on manifolds. - Yueqi Cao; Didong Li; Huafei Sun; Amir H. Assadi; Shiqiang Zhang (Mach. Learn., 2021)

Spherical Rotation Dimension Reduction with Geometric Loss Functions. - Hengrui Luo; Didong Li (CoRR, 2022)

Exponential-Wrapped Distributions on Symmetric Spaces. - Emmanuel Chevallier; Didong Li; Yulong Lu; David B. Dunson (SIAM J. Math. Data Sci., 2022)

On the Identifiability and Interpretability of Gaussian Process Models. - Jiawen Chen 002; Wancen Mu; Yun Li; Didong Li (NeurIPS, 2023)

Kernel Density Bayesian Inverse Reinforcement Learning. - Aishwarya Mandyam; Didong Li; Diana Cai; Andrew Jones; Barbara E. Engelhardt (CoRR, 2023)

Contrastive inverse regression for dimension reduction. - Sam Hawke; Hengrui Luo; Didong Li (CoRR, 2023)

Inference for Gaussian Processes with Matern Covariogram on Compact Riemannian Manifolds. - Didong Li; Wenpin Tang; Sudipto Banerjee (J. Mach. Learn. Res., 2023)

STimage-1K4M: A histopathology image-gene expression dataset for spatial transcriptomics. - Jiawen Chen 002; Muqing Zhou; Wenrong Wu; Jinwei Zhang; Yun Li; Didong Li (NeurIPS, 2024)

Contrastive dimension reduction: when and how? - Sam Hawke; YueEn Ma 002; Didong Li (NeurIPS, 2024)

Analysis of the ICML 2023 Ranking Data: Can Authors' Opinions of Their Own Papers Assist Peer Review in Machine Learning? - Buxin Su; Jiayao Zhang 001; Natalie Collina; Yuling Yan; Didong Li; Kyunghyun Cho; Jianqing Fan; Aaron Roth 001; Weijie J. Su (CoRR, 2024)

A Novel Compact LLM Framework for Local, High-Privacy EHR Data Applications. - Yixiang Qu; Yifan Dai; Shilin Yu; Pradham Tanikella; Travis Schrank; Trevor Hackman; Didong Li; Di Wu (CoRR, 2024)

Spherical Rotation Dimension Reduction with Geometric Loss Functions. - Hengrui Luo; Jeremy E. Purvis; Didong Li (J. Mach. Learn. Res., 2024)

KD-BIRL: Kernel Density Bayesian Inverse Reinforcement Learning. - Aishwarya Mandyam; Didong Li; Andrew Jones; Diana Cai; Barbara E. Engelhardt (Trans. Mach. Learn. Res., 2024)

Contrastive Functional Principal Component Analysis. - Eric Zhang; Didong Li (AAAI, 2025)

Identifiability for Gaussian Processes with Holomorphic Kernels. - Ameer Qaqish; Didong Li (ICLR, 2025)

Lower Ricci Curvature for Efficient Community Detection. - Yun Jin Park; Didong Li (Trans. Mach. Learn. Res., 2025)

Deep Generative Models: Complexity, Dimensionality, and Approximation. - Kevin Wang; Hongqian Niu; Yixin Wang; Didong Li (CoRR, 2025)

HR-VILAGE-3K3M: A Human Respiratory Viral Immunization Longitudinal Gene Expression Dataset for Systems Immunity. - Xuejun Sun; Yiran Song; Xiaochen Zhou; Ruilie Cai; Yu Zhang; Xinyi Li; Rui Peng; Jialiu Xie; Yuanyuan Yan; Muyao Tang; Prem Lakshmanane; Baiming Zou; et al. (CoRR, 2025)

Lower Ricci Curvature for Hypergraphs. - Shiyi Yang; Can Chen; Didong Li (CoRR, 2025)