Research

Publications

1. Y. Li and M. Yan, On the improved conditions for some primal-dual algorithms, 2022.

2. Y. Li, X. Liu, J. Tang, M. Yan and K. Yuan, Decentralized composite optimization with compression, 2021.

3. H. Tang, Y. Li, J. Liu and M. Yan, ErrorCompensatedX: error compensation for variance reduced algorithms, accepted by NeurIPS 2021.

4. X. Liu, Y. Li, R. Wang, J. Tang and M. Yan, Linear convergent decentralized optimization with compression, The Ninth International Conference on Learning Representations, 2021. (slides)

5. X. Liu, Y. Li, J. Tang and M. Yan, A double residual compression algorithm for efficient distributed learning, Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, PMLR 108:133-143, 2020. (slides)

6. Y. Li and M. Yan, On the linear convergence of two decentralized algorithms, Journal of Optimization Theory and Applications, pages 1-20, 2021.

Presentations & Talks

1. Title: Decentralized Composite Optimization with Compression (slides)
   Greenville, SC, Mar 2022
   

2. Tutorial: Communication Efficient Distributed Learning (slides)
   Montreal-themed Virtual Reality, Aug 2021
   

3. Title: A Communication Compression Decentralized Algorithm for Convex Composite Optimization (slides)
   Lehigh University, Bethlehem, PA, Aug 2021
   

4. Title: Preconditioned ADMM on (Convolutional) Sparse Coding (slides)
   Los Alamos National Laboratory, Los Alamos, NM, Aug 2019