Zhize Li
21 papers · 2015–2025 · 8 conferences · across top CS/AI conferences
Achievements
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π Conference Polyglot (8) πΊοΈ Taxonomy Completionist (10) π§ Keyword Pioneer π Interdisciplinary Bridge π Academic Marathon (10)
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Interdisciplinary Bridge
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Conference Polyglot
(8)
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Academic Marathon
(10)
π¬
Deep Specialist
(15)
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Grand Slam
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Keyword Champion
(2)
ποΈ
Keyword Collector
(68)
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Century Club
(21)
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Unstoppable
(5)
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Trend Setter
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Prolific Year
(5)
Conferences
NIPS (8)
ICML (4)
AISTATS (2)
IJCAI (2)
JMLR (2)
AAAI (1)
COLT (1)
ICLR (1)
Top co-authors
Research topics
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