Didong Li

According to our database, Didong Li authored at least 33 papers between 2017 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2026

Rejoinder: The ICML 2023 Ranking Experiment: Examining Author Self-Assessment in ML/AI Peer Review.

Buxin Su, Jiayao Zhang, Natalie Collina, Yuling Yan, Didong Li, Kyunghyun Cho, Jianqing Fan, Aaron Roth, Weijie J. Su

CoRR, May, 2026

NIH-MPINet: A Large-Scale Feature-Rich Network Dataset for Mapping the Frontiers of Team Science.

Cuiran Shi, Shuying Han, Shreya Kusumanchi, Mia Zhou, Didong Li

CoRR, April, 2026

Can Generative Artificial Intelligence Survive Data Contamination? Theoretical Guarantees under Contaminated Recursive Training.

Kevin Wang, Hongqian Niu, Didong Li

CoRR, February, 2026

2025

Understanding Overparametrization in Survival Models through Interpolation.

Yin Liu, Jianwen Cai, Didong Li

CoRR, December, 2025

Large-scale spatial variable gene atlas for spatial transcriptomics.

Jiawen Chen, Jinwei Zhang, Dongshen Peng, Yutong Song, Aitong Ruan, Yun Li, Didong Li

CoRR, October, 2025

How to Find Fantastic Papers: Self-Rankings as a Powerful Predictor of Scientific Impact Beyond Peer Review.

Buxin Su, Natalie Collina, Garrett Wen, Didong Li, Kyunghyun Cho, Jianqing Fan, Bingxin Zhao, Weijie Su

CoRR, October, 2025

Incorporating LLM Embeddings for Variation Across the Human Genome.

Hongqian Niu, Jordan Bryan, Xihao Li, Didong Li

CoRR, September, 2025

Lower Ricci Curvature for Hypergraphs.

Shiyi Yang, Can Chen, Didong Li

CoRR, June, 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, James S. Hagood, Raymond J. Pickles, Didong Li, Fei Zou, Xiaojing Zheng

CoRR, May, 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

J. Mach. Learn. Res., 2025

Bayesian Multi-Group Gaussian Process Models for Heterogeneous Group-Structured Data.

Didong Li, Andrew Jones, Sudipto Banerjee, Barbara E. Engelhardt

J. Mach. Learn. Res., 2025

Identifiability for Gaussian Processes with Holomorphic Kernels.

Ameer Qaqish, Didong Li

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

Contrastive Functional Principal Component Analysis.

Eric Zhang, Didong Li

Proceedings of the Thirty-Ninth AAAI Conference on Artificial Intelligence, 2025

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

Spherical Rotation Dimension Reduction with Geometric Loss Functions.

Hengrui Luo, Jeremy E. Purvis, Didong Li

J. Mach. Learn. Res., 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

Analysis of the ICML 2023 Ranking Data: Can Authors' Opinions of Their Own Papers Assist Peer Review in Machine Learning?

Buxin Su, Jiayao Zhang, Natalie Collina, Yuling Yan, Didong Li, Kyunghyun Cho, Jianqing Fan, Aaron Roth, Weijie J. Su

CoRR, 2024

Contrastive dimension reduction: when and how?

Sam Hawke, YueEn Ma, Didong Li

Proceedings of the Advances in Neural Information Processing Systems 37: Annual Conference on Neural Information Processing Systems 2024, 2024

STimage-1K4M: A histopathology image-gene expression dataset for spatial transcriptomics.

Jiawen Chen, Muqing Zhou, Wenrong Wu, Jinwei Zhang, Yun Li, Didong Li

Proceedings of the Advances in Neural Information Processing Systems 37: Annual Conference on Neural Information Processing Systems 2024, 2024

2023

Inference for Gaussian Processes with Matern Covariogram on Compact Riemannian Manifolds.

Didong Li, Wenpin Tang, Sudipto Banerjee

J. Mach. Learn. Res., 2023

Contrastive inverse regression for dimension reduction.

Sam Hawke, Hengrui Luo, Didong Li

CoRR, 2023

Kernel Density Bayesian Inverse Reinforcement Learning.

Aishwarya Mandyam, Didong Li, Diana Cai, Andrew Jones, Barbara E. Engelhardt

CoRR, 2023

On the Identifiability and Interpretability of Gaussian Process Models.

Jiawen Chen, Wancen Mu, Yun Li, Didong Li

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022

Exponential-Wrapped Distributions on Symmetric Spaces.

Emmanuel Chevallier, Didong Li, Yulong Lu, David B. Dunson

SIAM J. Math. Data Sci., December, 2022

Spherical Rotation Dimension Reduction with Geometric Loss Functions.

Hengrui Luo, Didong Li

CoRR, 2022

2021

Efficient Weingarten map and curvature estimation on manifolds.

Yueqi Cao, Didong Li, Huafei Sun, Amir H. Assadi, Shiqiang Zhang

Mach. Learn., 2021

From the Greene-Wu Convolution to Gradient Estimation over Riemannian Manifolds.

Tianyu Wang, Yifeng Huang, Didong Li

CoRR, 2021

2020

A Geometric Approach to Average Problems on Multinomial and Negative Multinomial Models.

Mingming Li, Huafei Sun, Didong Li

Entropy, 2020

Probabilistic Contrastive Principal Component Analysis.

Didong Li, Andrew Jones, Barbara E. Engelhardt

CoRR, 2020

2019

Efficient Curvature Estimation for Oriented Point Clouds.

Yueqi Cao, Didong Li, Huafei Sun, Amir H. Assadi, Shiqiang Zhang

CoRR, 2019

Classification via local manifold approximation.

Didong Li, David B. Dunson

CoRR, 2019

2017

A Geodesic-Based Riemannian Gradient Approach to Averaging on the Lorentz Group.

Jing Wang, Huafei Sun, Didong Li

Entropy, 2017