36 citations · 130 across the 23 of their papers we have counts for
5 papers · 1 filter
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…