← Authors

Zhize Li

21 papers · 2015–2025 · 8 conferences · across top CS/AI conferences

Achievements

🌈 Renaissance Researcher (6) πŸ—ΊοΈ Taxonomy Completionist (10) 🐣 Hot Topic Early Bird 🐝 Cross-Pollinator (11) 🧭 Keyword Pioneer πŸŒ‰ Interdisciplinary Bridge 🌍 Conference Polyglot (8) πŸƒ Academic Marathon (10) πŸ”¬ Deep Specialist (15) πŸ† Grand Slam πŸ† Keyword Champion (2) πŸ—ƒοΈ Keyword Collector (68) πŸ’Ž Century Club (21) πŸ”₯ Unstoppable (5) πŸ“ˆ Trend Setter ⚑ Prolific Year (5)

Conferences

NIPS (8) ICML (4) AISTATS (2) IJCAI (2) JMLR (2) AAAI (1) COLT (1) ICLR (1)

Top co-authors

Peter Richtarik (7) Jian Li (5) Eduard Gorbunov (3) Haoyu Zhao (3) Yuejie Chi (3) Xiang Wang (2) Hongyan Bao (2) Igor Sokolov (2) Ilyas Fatkhullin (2) Rong Ge (2)

Research topics

Differential Privacy (1)

Keywords

nonconvex optimization (7) federated learning (7) distributed optimization (7) communication compression (7) variance reduction (6) stochastic gradient descent (5) error feedback (4) stochastic gradient (4) gradient descent (4) convex optimization (3) finite-sum optimization (2) gradient estimator (2) saddle point (2) optimal convergence rate (2) proximal gradient (2) linear convergence (2) convergence rate (2) accelerated gradient (2) nonsmooth optimization (2) second-order stationary point (2)

Papers

EFSkip: A New Error Feedback with Linear Speedup for Compressed Federated Learning with Arbitrary Data Heterogeneity AAAI 2025
EF21 with Bells & Whistles: Six Algorithmic Extensions of Modern Error Feedback JMLR 2025
SIFAR: A Simple Faster Accelerated Variance-Reduced Gradient Method IJCAI 2025
Escaping Saddle Points in Heterogeneous Federated Learning via Distributed SGD with Communication Compression AISTATS 2024
Coresets for Vertical Federated Learning: Regularized Linear Regression and $K$-Means Clustering NIPS 2022
BEER: Fast $O(1/T)$ Rate for Decentralized Nonconvex Optimization with Communication Compression NIPS 2022
SoteriaFL: A Unified Framework for Private Federated Learning with Communication Compression NIPS 2022
Simple and Optimal Stochastic Gradient Methods for Nonsmooth Nonconvex Optimization JMLR 2022
3PC: Three Point Compressors for Communication-Efficient Distributed Training and a Better Theory for Lazy Aggregation ICML 2022
MARINA: Faster Non-Convex Distributed Learning with Compression ICML 2021
CANITA: Faster Rates for Distributed Convex Optimization with Communication Compression NIPS 2021
PAGE: A Simple and Optimal Probabilistic Gradient Estimator for Nonconvex Optimization ICML 2021
Acceleration for Compressed Gradient Descent in Distributed and Federated Optimization ICML 2020
A Fast Anderson-Chebyshev Acceleration for Nonlinear Optimization AISTATS 2020
Learning Two-layer Neural Networks with Symmetric Inputs ICLR 2019
SSRGD: Simple Stochastic Recursive Gradient Descent for Escaping Saddle Points NIPS 2019
Stabilized SVRG: Simple Variance Reduction for Nonconvex Optimization COLT 2019
Gradient Boosting with Piece-Wise Linear Regression Trees IJCAI 2019
A unified variance-reduced accelerated gradient method for convex optimization NIPS 2019
A Simple Proximal Stochastic Gradient Method for Nonsmooth Nonconvex Optimization NIPS 2018
On Top-k Selection in Multi-Armed Bandits and Hidden Bipartite Graphs NIPS 2015