On Top-k Selection in Multi-Armed Bandits and Hidden Bipartite Graphs. - Wei Cao 007; Jian Li 015; Yufei Tao 001; Zhize Li (NIPS, 2015)

Modeling and Routing for Predictable Dynamic Networks. - Zengyin Yang; Qian Wu 001; Zhize Li; Hewu Li; Jianping Wu (CoRR, 2017)

A Simple Proximal Stochastic Gradient Method for Nonsmooth Nonconvex Optimization. - Zhize Li; Jian Li 015 (NeurIPS, 2018)

A Two-Stage Mechanism for Ordinal Peer Assessment. - Zhize Li; Le Zhang; Zhixuan Fang; Jian Li 015 (SAGT, 2018)

Stochastic Gradient Hamiltonian Monte Carlo with Variance Reduction for Bayesian Inference. - Zhize Li; Tianyi Zhang 008; Jian Li 015 (CoRR, 2018)

An Anderson-Chebyshev Mixing Method for Nonlinear Optimization. - Zhize Li; Jian Li 015 (CoRR, 2018)

A Fast Polynomial-time Primal-Dual Projection Algorithm for Linear Programming. - Zhize Li; Wei Zhang; Kees Roos (CoRR, 2018)

Stabilized SVRG: Simple Variance Reduction for Nonconvex Optimization. - Rong Ge 001; Zhize Li; Weiyao Wang 001; Xiang Wang 011 (COLT, 2019)

Learning Two-layer Neural Networks with Symmetric Inputs. - Rong Ge 001; Rohith Kuditipudi; Zhize Li; Xiang Wang 011 (ICLR (Poster), 2019)

Gradient Boosting with Piece-Wise Linear Regression Trees. - Yu Shi; Jian Li 015; Zhize Li (IJCAI, 2019)

A unified variance-reduced accelerated gradient method for convex optimization. - Guanghui Lan; Zhize Li; Yi Zhou 015 (NeurIPS, 2019)

SSRGD: Simple Stochastic Recursive Gradient Descent for Escaping Saddle Points. - Zhize Li (NeurIPS, 2019)

Stochastic gradient Hamiltonian Monte Carlo with variance reduction for Bayesian inference. - Zhize Li; Tianyi Zhang 008; Shuyu Cheng; Jun Zhu 001; Jian Li 015 (Mach. Learn., 2019)

A Fast Anderson-Chebyshev Acceleration for Nonlinear Optimization. - Zhize Li; Jian Li 015 (AISTATS, 2020)

Acceleration for Compressed Gradient Descent in Distributed and Federated Optimization. - Zhize Li; Dmitry Kovalev; Xun Qian; Peter Richtárik (ICML, 2020)

A Unified Analysis of Stochastic Gradient Methods for Nonconvex Federated Optimization. - Zhize Li; Peter Richtárik (CoRR, 2020)

MARINA: Faster Non-Convex Distributed Learning with Compression. - Eduard Gorbunov; Konstantin Burlachenko; Zhize Li; Peter Richtárik (ICML, 2021)

PAGE: A Simple and Optimal Probabilistic Gradient Estimator for Nonconvex Optimization. - Zhize Li; Hongyan Bao; Xiangliang Zhang 001; Peter Richtárik (ICML, 2021)

CANITA: Faster Rates for Distributed Convex Optimization with Communication Compression. - Zhize Li; Peter Richtárik (NeurIPS, 2021)

ZeroSARAH: Efficient Nonconvex Finite-Sum Optimization with Zero Full Gradient Computation. - Zhize Li; Peter Richtárik (CoRR, 2021)

ANITA: An Optimal Loopless Accelerated Variance-Reduced Gradient Method. - Zhize Li (CoRR, 2021)

A Short Note of PAGE: Optimal Convergence Rates for Nonconvex Optimization. - Zhize Li (CoRR, 2021)

FedPAGE: A Fast Local Stochastic Gradient Method for Communication-Efficient Federated Learning. - Haoyu Zhao; Zhize Li; Peter Richtárik (CoRR, 2021)

EF21 with Bells & Whistles: Practical Algorithmic Extensions of Modern Error Feedback. - Ilyas Fatkhullin; Igor Sokolov 001; Eduard Gorbunov; Zhize Li; Peter Richtárik (CoRR, 2021)

3PC: Three Point Compressors for Communication-Efficient Distributed Training and a Better Theory for Lazy Aggregation. - Peter Richtárik; Igor Sokolov 001; Elnur Gasanov; Ilyas Fatkhullin; Zhize Li; Eduard Gorbunov (ICML, 2022)

Coresets for Vertical Federated Learning: Regularized Linear Regression and $K$-Means Clustering. - Lingxiao Huang; Zhize Li; Jialin Sun; Haoyu Zhao (NeurIPS, 2022)

SoteriaFL: A Unified Framework for Private Federated Learning with Communication Compression. - Zhize Li; Haoyu Zhao; Boyue Li; Yuejie Chi (NeurIPS, 2022)

BEER: Fast $O(1/T)$ Rate for Decentralized Nonconvex Optimization with Communication Compression. - Haoyu Zhao; Boyue Li; Zhize Li; Peter Richtárik; Yuejie Chi (NeurIPS, 2022)

Optimal in-place suffix sorting. - Zhize Li; Jian Li 015; Hongwei Huo (Inf. Comput., 2022)

Simple and Optimal Stochastic Gradient Methods for Nonsmooth Nonconvex Optimization. - Zhize Li; Jian Li 015 (J. Mach. Learn. Res., 2022)

DESTRESS: Computation-Optimal and Communication-Efficient Decentralized Nonconvex Finite-Sum Optimization. - Boyue Li; Zhize Li; Yuejie Chi (SIAM J. Math. Data Sci., 2022)

Escaping Saddle Points in Heterogeneous Federated Learning via Distributed SGD with Communication Compression. - Sijin Chen; Zhize Li; Yuejie Chi (AISTATS, 2024)

Faster Rates for Compressed Federated Learning with Client-Variance Reduction. - Haoyu Zhao; Konstantin Burlachenko; Zhize Li; Peter Richtárik (SIAM J. Math. Data Sci., 2024)

EFSkip: A New Error Feedback with Linear Speedup for Compressed Federated Learning with Arbitrary Data Heterogeneity. - Hongyan Bao; Pengwen Chen; Ying Sun 003; Zhize Li (AAAI, 2025)

Efficient Spectral-Spatial Fusion With Multiscale and Adaptive Attention for Hyperspectral Image Classification. - Xiaoqing Wan; Feng Chen; Weizhe Gao; Yupeng He; Hui Liu; Zhize Li (IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., 2025)

The Effectiveness of Local Updates for Decentralized Learning Under Data Heterogeneity. - Tongle Wu; Zhize Li; Ying Sun 003 (IEEE Trans. Signal Process., 2025)

On the Trustworthiness of Generative Foundation Models: Guideline, Assessment, and Perspective. - Yue Huang 001; Chujie Gao; Siyuan Wu 001; Haoran Wang 005; Xiangqi Wang; Yujun Zhou 010; Yanbo Wang 005; Jiayi Ye; Jiawen Shi; Qihui Zhang; Yuan Li 032; Han Bao 003; et al. (CoRR, 2025)

X-VFL: A New Vertical Federated Learning Framework with Cross Completion and Decision Subspace Alignment. - Qinghua Yao; Xiangrui Xu 001; Zhize Li (CoRR, 2025)