36 citations · 130 across the 23 of their papers we have counts for
24 papers
FedComLoc: Communication-Efficient Distributed Training of Sparse and Quantized Models
Kai Yi, Georg Meinhardt, Laurent Condat +1
Federated Learning (FL) has garnered increasing attention due to its unique characteristic of allowing heterogeneous clients to process their private data locally and interact with…
LoCoDL: Communication-Efficient Distributed Learning with Local Training and Compression
Laurent Condat, Artavazd Maranjyan, Peter Richtárik
In Distributed optimization and Learning, and even more in the modern framework of federated learning, communication, which is slow and costly, is critical. We introduce LoCoDL, a…
Achieving Linear Speedup with ProxSkip in Distributed Stochastic Optimization
Luyao Guo, Sulaiman A. Alghunaim, Kun Yuan +2
The ProxSkip algorithm for distributed optimization is gaining increasing attention due to its effectiveness in reducing communication. However, existing analyses of ProxSkip are l…
Near-Linear Time Projection onto the Ball; Application to Sparse Autoencoders
Guillaume Perez, Laurent Condat, Michel Barlaud
Looking for sparsity is nowadays crucial to speed up the training of large-scale neural networks. Projections onto the and are among the most efficie…
Explicit Personalization and Local Training: Double Communication Acceleration in Federated Learning
Kai Yi, Laurent Condat, Peter Richtárik
Federated Learning is an evolving machine learning paradigm, in which multiple clients perform computations based on their individual private data, interspersed by communication wi…
TAMUNA: Doubly Accelerated Distributed Optimization under Partial Participation
Laurent Condat, Ivan Agarský, Grigory Malinovsky +1
In distributed optimization and federated learning, slow and costly communication between parallel devices and the central server constitutes the primary bottleneck. To alleviate t…
CompressedScaffnew: The First Theoretical Double Acceleration of Communication from Local Training and Compression in Distributed Optimization
Laurent Condat, Ivan Agarský, Peter Richtárik
In distributed optimization, a large number of machines alternate between local computations and communication with a coordinating server. Communication, which can be slow and cost…
Joint demosaicing and fusion of multiresolution coded acquisitions: A unified image formation and reconstruction method
Daniele Picone, Mauro Dalla Mura, Laurent Condat
Novel optical imaging devices allow for hybrid acquisition modalities such as compressed acquisitions with locally different spatial and spectral resolutions captured by a single f…
Tikhonov Regularization of Sphere-Valued Signals
Laurent Condat
It is common to have to process signals, whose values are points on the 3-D sphere. We consider a Tikhonov-type regularization model to smoothen or interpolate sphere-valued signal…
RandProx: Primal-Dual Optimization Algorithms with Randomized Proximal Updates
Laurent Condat, Peter Richtárik
Proximal splitting algorithms are well suited to solving large-scale nonsmooth optimization problems, in particular those arising in machine learning. We propose a new primal-dual…
EF-BV: A Unified Theory of Error Feedback and Variance Reduction Mechanisms for Biased and Unbiased Compression in Distributed Optimization
Laurent Condat, Kai Yi, Peter Richtárik
In distributed or federated optimization and learning, communication between the different computing units is often the bottleneck and gradient compression is widely used to reduce…
Tikhonov Regularization of Circle-Valued Signals
Laurent Condat
It is common to have to process signals or images whose values are cyclic and can be represented as points on the complex circle, like wrapped phases, angles, orientations, or colo…
MURANA: A Generic Framework for Stochastic Variance-Reduced Optimization
Laurent Condat, Peter Richtárik
We propose a generic variance-reduced algorithm, which we call MUltiple RANdomized Algorithm (MURANA), for minimizing a sum of several smooth functions plus a regularizer, in a seq…
An Optimal Algorithm for Strongly Convex Minimization under Affine Constraints
Adil Salim, Laurent Condat, Dmitry Kovalev +1
Optimization problems under affine constraints appear in various areas of machine learning. We consider the task of minimizing a smooth strongly convex function F(x) under the affi…
Optimal Gradient Compression for Distributed and Federated Learning
Alyazeed Albasyoni, Mher Safaryan, Laurent Condat +1
Communicating information, like gradient vectors, between computing nodes in distributed and federated learning is typically an unavoidable burden, resulting in scalability issues.…
Distributed Proximal Splitting Algorithms with Rates and Acceleration
Laurent Condat, Grigory Malinovsky, Peter Richtárik
We analyze several generic proximal splitting algorithms well suited for large-scale convex nonsmooth optimization. We derive sublinear and linear convergence results with new rate…
Dualize, Split, Randomize: Toward Fast Nonsmooth Optimization Algorithms
Adil Salim, Laurent Condat, Konstantin Mishchenko +1
We consider minimizing the sum of three convex functions, where the first one F is smooth, the second one is nonsmooth and proximable and the third one is the composition of a nons…
From Local SGD to Local Fixed-Point Methods for Federated Learning
Grigory Malinovsky, Dmitry Kovalev, Elnur Gasanov +2
Most algorithms for solving optimization problems or finding saddle points of convex-concave functions are fixed-point algorithms. In this work we consider the generic problem of f…
Proximal Splitting Algorithms for Convex Optimization: A Tour of Recent Advances, with New Twists
Laurent Condat, Daichi Kitahara, Andrés Contreras +1
Convex nonsmooth optimization problems, whose solutions live in very high dimensional spaces, have become ubiquitous. To solve them, the class of first-order algorithms known as pr…
Riesz-based orientation of localizable Gaussian fields
Kévin Polisano, Marianne Clausel, Valérie Perrier +1
In this work we give a sense to the notion of orientation for self-similar Gaussian fields with stationary increments, based on a Riesz analysis of these fields, with isotropic zer…
On-the-fly Approximation of Multivariate Total Variation Minimization
Jordan Frecon, Nelly Pustelnik, Patrice Abry +1
In the context of change-point detection, addressed by Total Variation minimization strategies, an efficient on-the-fly algorithm has been designed leading to exact solutions for u…
A forward-backward view of some primal-dual optimization methods in image recovery
Patrick L. Combettes, Laurent Condat, Jean-Christophe Pesquet +1
A wide array of image recovery problems can be abstracted into the problem of minimizing a sum of composite convex functions in a Hilbert space. To solve such problems, primal-dual…
Texture Modeling by Gaussian fields with prescribed local orientation
Kévin Polisano, Marianne Clausel, Valérie Perrier +1
This paper presents a new framework for oriented texture modeling. We introduce a new class of Gaussian fields, called Locally Anisotropic Fractional Brownian Fields, with prescrib…
2-D Prony-Huang Transform: A New Tool for 2-D Spectral Analysis
Jérémy Schmitt, Nelly Pustelnik, Pierre Borgnat +2
This work proposes an extension of the 1-D Hilbert Huang transform for the analysis of images. The proposed method consists in (i) adaptively decomposing an image into oscillating…