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10/26/2022
Coresets for Vertical Federated Learning: Regularized Linear Regression and K-Means Clustering
Vertical federated learning (VFL), where data features are stored in mul...
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08/22/2022
Simple and Optimal Stochastic Gradient Methods for Nonsmooth Nonconvex Optimization
We propose and analyze several stochastic gradient algorithms for findin...
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06/20/2022
SoteriaFL: A Unified Framework for Private Federated Learning with Communication Compression
To enable large-scale machine learning in bandwidth-hungry environments ...
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02/02/2022
3PC: Three Point Compressors for Communication-Efficient Distributed Training and a Better Theory for Lazy Aggregation
We propose and study a new class of gradient communication mechanisms fo...
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01/31/2022
BEER: Fast O(1/T) Rate for Decentralized Nonconvex Optimization with Communication Compression
Communication efficiency has been widely recognized as the bottleneck fo...
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12/24/2021
Faster Rates for Compressed Federated Learning with Client-Variance Reduction
Due to the communication bottleneck in distributed and federated learnin...
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10/07/2021
EF21 with Bells Whistles: Practical Algorithmic Extensions of Modern Error Feedback
First proposed by Seide (2014) as a heuristic, error feedback (EF) is a ...
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10/04/2021
DESTRESS: Computation-Optimal and Communication-Efficient Decentralized Nonconvex Finite-Sum Optimization
Emerging applications in multi-agent environments such as internet-of-th...
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08/10/2021
FedPAGE: A Fast Local Stochastic Gradient Method for Communication-Efficient Federated Learning
Federated Averaging (FedAvg, also known as Local-SGD) (McMahan et al., 2...
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07/20/2021
CANITA: Faster Rates for Distributed Convex Optimization with Communication Compression
Due to the high communication cost in distributed and federated learning...
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06/17/2021
A Short Note of PAGE: Optimal Convergence Rates for Nonconvex Optimization
In this note, we first recall the nonconvex problem setting and introduc...
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03/21/2021
ANITA: An Optimal Loopless Accelerated Variance-Reduced Gradient Method
We propose a novel accelerated variance-reduced gradient method called A...
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03/02/2021
ZeroSARAH: Efficient Nonconvex Finite-Sum Optimization with Zero Full Gradient Computation
We propose ZeroSARAH – a novel variant of the variance-reduced method SA...
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02/15/2021
MARINA: Faster Non-Convex Distributed Learning with Compression
We develop and analyze MARINA: a new communication efficient method for ...
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08/25/2020
PAGE: A Simple and Optimal Probabilistic Gradient Estimator for Nonconvex Optimization
In this paper, we propose a novel stochastic gradient estimator—ProbAbil...
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06/12/2020
A Unified Analysis of Stochastic Gradient Methods for Nonconvex Federated Optimization
In this paper, we study the performance of a large family of SGD variant...
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02/26/2020
Acceleration for Compressed Gradient Descent in Distributed and Federated Optimization
Due to the high communication cost in distributed and federated learning...
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05/29/2019
A unified variance-reduced accelerated gradient method for convex optimization
We propose a novel randomized incremental gradient algorithm, namely, VA...
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05/01/2019
Stabilized SVRG: Simple Variance Reduction for Nonconvex Optimization
Variance reduction techniques like SVRG provide simple and fast algorith...
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04/19/2019
SSRGD: Simple Stochastic Recursive Gradient Descent for Escaping Saddle Points
We analyze stochastic gradient algorithms for optimizing nonconvex probl...
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10/16/2018
Learning Two-layer Neural Networks with Symmetric Inputs
We give a new algorithm for learning a two-layer neural network under a ...
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10/10/2018
A Fast Polynomial-time Primal-Dual Projection Algorithm for Linear Programming
Traditionally, there are several polynomial algorithms for linear progra...
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09/07/2018
A Fast Anderson-Chebyshev Mixing Method for Nonlinear Optimization
Anderson mixing (or Anderson acceleration) is an efficient acceleration ...
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09/07/2018
An Anderson-Chebyshev Mixing Method for Nonlinear Optimization
Anderson mixing (or Anderson acceleration) is an efficient acceleration ...
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03/29/2018
Stochastic Gradient Hamiltonian Monte Carlo with Variance Reduction for Bayesian Inference
Gradient-based Monte Carlo sampling algorithms, like Langevin dynamics a...
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02/15/2018
Gradient Boosting With Piece-Wise Linear Regression Trees
Gradient boosting using decision trees as base learners, so called Gradi...
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02/13/2018
