
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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EF21: A New, Simpler, Theoretically Better, and Practically Faster Error Feedback
Error feedback (EF), also known as error compensation, is an immensely p...
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Lower Bounds and Optimal Algorithms for Smooth and Strongly Convex Decentralized Optimization Over TimeVarying Networks
We consider the task of minimizing the sum of smooth and strongly convex...
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SmoothnessAware Quantization Techniques
Distributed machine learning has become an indispensable tool for traini...
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Complexity Analysis of Stein Variational Gradient Descent Under Talagrand's Inequality T1
We study the complexity of Stein Variational Gradient Descent (SVGD), wh...
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MURANA: A Generic Framework for Stochastic VarianceReduced Optimization
We propose a generic variancereduced algorithm, which we call MUltiple ...
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FedNL: Making NewtonType Methods Applicable to Federated Learning
Inspired by recent work of Islamov et al (2021), we propose a family of ...
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Random Reshuffling with Variance Reduction: New Analysis and Better Rates
Virtually all stateoftheart methods for training supervised machine l...
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ZeroSARAH: Efficient Nonconvex FiniteSum Optimization with Zero Full Gradient Computation
We propose ZeroSARAH – a novel variant of the variancereduced method SA...
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Hyperparameter Transfer Learning with Adaptive Complexity
Bayesian optimization (BO) is a sample efficient approach to automatical...
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AISARAH: Adaptive and Implicit Stochastic Recursive Gradient Methods
We present an adaptive stochastic variance reduced method with an implic...
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ADOM: Accelerated Decentralized Optimization Method for TimeVarying Networks
We propose ADOM  an accelerated method for smooth and strongly convex d...
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IntSGD: Floatless Compression of Stochastic Gradients
We propose a family of lossy integer compressions for Stochastic Gradien...
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MARINA: Faster NonConvex Distributed Learning with Compression
We develop and analyze MARINA: a new communication efficient method for ...
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Smoothness Matrices Beat Smoothness Constants: Better Communication Compression Techniques for Distributed Optimization
Large scale distributed optimization has become the default tool for the...
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Distributed Second Order Methods with Fast Rates and Compressed Communication
We develop several new communicationefficient secondorder methods for ...
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Proximal and Federated Random Reshuffling
Random Reshuffling (RR), also known as Stochastic Gradient Descent (SGD)...
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A Linearly Convergent Algorithm for Decentralized Optimization: Sending Less Bits for Free!
Decentralized optimization methods enable ondevice training of machine ...
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Local SGD: Unified Theory and New Efficient Methods
We present a unified framework for analyzing local SGD methods in the co...
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Optimal Client Sampling for Federated Learning
It is well understood that clientmaster communication can be a primary ...
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Linearly Converging Error Compensated SGD
In this paper, we propose a unified analysis of variants of distributed ...
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Optimal Gradient Compression for Distributed and Federated Learning
Communicating information, like gradient vectors, between computing node...
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Lower Bounds and Optimal Algorithms for Personalized Federated Learning
In this work, we consider the optimization formulation of personalized f...
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Distributed Proximal Splitting Algorithms with Rates and Acceleration
We analyze several generic proximal splitting algorithms well suited for...
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VarianceReduced Methods for Machine Learning
Stochastic optimization lies at the heart of machine learning, and its c...
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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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Optimal and Practical Algorithms for Smooth and Strongly Convex Decentralized Optimization
We consider the task of decentralized minimization of the sum of smooth ...
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Unified Analysis of Stochastic Gradient Methods for Composite Convex and Smooth Optimization
We present a unified theorem for the convergence analysis of stochastic ...
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A Better Alternative to Error Feedback for CommunicationEfficient Distributed Learning
Modern largescale machine learning applications require stochastic opti...
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Primal Dual Interpretation of the Proximal Stochastic Gradient Langevin Algorithm
We consider the task of sampling with respect to a log concave probabili...
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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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Random Reshuffling: Simple Analysis with Vast Improvements
Random Reshuffling (RR) is an algorithm for minimizing finitesum functi...
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Adaptive Learning of the Optimal MiniBatch Size of SGD
Recent advances in the theoretical understandingof SGD (Qian et al., 201...
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On the Convergence Analysis of Asynchronous SGD for Solving Consistent Linear Systems
In the realm of big data and machine learning, dataparallel, distribute...
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Dualize, Split, Randomize: Fast Nonsmooth Optimization Algorithms
We introduce a new primaldual algorithm for minimizing the sum of three...
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From Local SGD to Local Fixed Point Methods for Federated Learning
Most algorithms for solving optimization problems or finding saddle poin...
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On Biased Compression for Distributed Learning
In the last few years, various communication compression techniques have...
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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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Fast Linear Convergence of Randomized BFGS
Since the late 1950's when quasiNewton methods first appeared, they hav...
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Stochastic Subspace Cubic Newton Method
In this paper, we propose a new randomized secondorder optimization alg...
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Uncertainty Principle for Communication Compression in Distributed and Federated Learning and the Search for an Optimal Compressor
In order to mitigate the high communication cost in distributed and fede...
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Adaptivity of Stochastic Gradient Methods for Nonconvex Optimization
Adaptivity is an important yet understudied property in modern optimiza...
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Variance Reduced Coordinate Descent with Acceleration: New Method With a Surprising Application to FiniteSum Problems
We propose an accelerated version of stochastic variance reduced coordin...
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Federated Learning of a Mixture of Global and Local Models
We propose a new optimization formulation for training federated learnin...
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Better Theory for SGD in the Nonconvex World
Largescale nonconvex optimization problems are ubiquitous in modern mac...
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Distributed Fixed Point Methods with Compressed Iterates
We propose basic and natural assumptions under which iterative optimizat...
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Stochastic Newton and Cubic Newton Methods with Simple Local LinearQuadratic Rates
We present two new remarkably simple stochastic secondorder methods for...
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Better Communication Complexity for Local SGD
We revisit the local Stochastic Gradient Descent (local SGD) method and ...
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Gradient Descent with Compressed Iterates
We propose and analyze a new type of stochastic first order method: grad...
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First Analysis of Local GD on Heterogeneous Data
We provide the first convergence analysis of local gradient descent for ...
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