![](https://dblp.uni-trier.de/img/logo.ua.320x120.png)
![](https://dblp.uni-trier.de/img/dropdown.dark.16x16.png)
![](https://dblp.uni-trier.de/img/peace.dark.16x16.png)
Остановите войну!
for scientists:
![search dblp search dblp](https://dblp.uni-trier.de/img/search.dark.16x16.png)
![search dblp](https://dblp.uni-trier.de/img/search.dark.16x16.png)
default search action
Laurent Condat
Person information
- affiliation: Grenoble Institute of Technology, France
Refine list
![note](https://dblp.uni-trier.de/img/note-mark.dark.12x12.png)
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
2020 – today
- 2024
- [i16]Laurent Condat, Artavazd Maranjyan, Peter Richtárik:
LoCoDL: Communication-Efficient Distributed Learning with Local Training and Compression. CoRR abs/2403.04348 (2024) - [i15]Kai Yi, Georg Meinhardt, Laurent Condat, Peter Richtárik:
FedComLoc: Communication-Efficient Distributed Training of Sparse and Quantized Models. CoRR abs/2403.09904 (2024) - [i14]Georg Meinhardt, Kai Yi, Laurent Condat, Peter Richtárik:
Prune at the Clients, Not the Server: Accelerated Sparse Training in Federated Learning. CoRR abs/2405.20623 (2024) - 2023
- [j30]Laurent Condat
, Daichi Kitahara, Andrés Contreras, Akira Hirabayashi:
Proximal Splitting Algorithms for Convex Optimization: A Tour of Recent Advances, with New Twists. SIAM Rev. 65(2): 375-435 (2023) - [j29]Daniele Picone
, Mauro Dalla Mura
, Laurent Condat
:
Joint Demosaicing and Fusion of Multiresolution Coded Acquisitions: A Unified Image Formation and Reconstruction Method. IEEE Trans. Computational Imaging 9: 335-349 (2023) - [c48]Laurent Condat, Peter Richtárik:
RandProx: Primal-Dual Optimization Algorithms with Randomized Proximal Updates. ICLR 2023 - [i13]Laurent Condat, Grigory Malinovsky, Peter Richtárik:
TAMUNA: Accelerated Federated Learning with Local Training and Partial Participation. CoRR abs/2302.09832 (2023) - [i12]Kai Yi, Laurent Condat, Peter Richtárik:
Explicit Personalization and Local Training: Double Communication Acceleration in Federated Learning. CoRR abs/2305.13170 (2023) - [i11]Guillaume Perez, Laurent Condat, Michel Barlaud:
Near-Linear Time Projection onto the 𝓁1, ∞ Ball; Application to Sparse Autoencoders. CoRR abs/2307.09836 (2023) - [i10]Luyao Guo, Sulaiman A. Alghunaim, Kun Yuan, Laurent Condat, Jinde Cao:
RandCom: Random Communication Skipping Method for Decentralized Stochastic Optimization. CoRR abs/2310.07983 (2023) - 2022
- [j28]Adil Salim, Laurent Condat
, Konstantin Mishchenko
, Peter Richtárik
:
Dualize, Split, Randomize: Toward Fast Nonsmooth Optimization Algorithms. J. Optim. Theory Appl. 195(1): 102-130 (2022) - [j27]Laurent Condat
:
Tikhonov Regularization of Circle-Valued Signals. IEEE Trans. Signal Process. 70: 2775-2782 (2022) - [c47]Adil Salim, Laurent Condat, Dmitry Kovalev, Peter Richtárik:
An Optimal Algorithm for Strongly Convex Minimization under Affine Constraints. AISTATS 2022: 4482-4498 - [c46]Laurent Condat, Peter Richtárik:
MURANA: A Generic Framework for Stochastic Variance-Reduced Optimization. MSML 2022: 81-96 - [c45]Laurent Condat, Kai Yi, Peter Richtárik:
EF-BV: A Unified Theory of Error Feedback and Variance Reduction Mechanisms for Biased and Unbiased Compression in Distributed Optimization. NeurIPS 2022 - [i9]Laurent Condat, Kai Yi, Peter Richtárik:
EF-BV: A Unified Theory of Error Feedback and Variance Reduction Mechanisms for Biased and Unbiased Compression in Distributed Optimization. CoRR abs/2205.04180 (2022) - [i8]Laurent Condat, Ivan Agarský, Peter Richtárik:
Provably Doubly Accelerated Federated Learning: The First Theoretically Successful Combination of Local Training and Compressed Communication. CoRR abs/2210.13277 (2022) - 2021
- [j26]Hiryu Kamoshita, Daichi Kitahara, Ken'ichi Fujimoto, Laurent Condat, Akira Hirabayashi:
Multiclass Dictionary-Based Statistical Iterative Reconstruction for Low-Dose CT. IEICE Trans. Fundam. Electron. Commun. Comput. Sci. 104-A(4): 702-713 (2021) - [i7]Laurent Condat, Peter Richtárik
:
MURANA: A Generic Framework for Stochastic Variance-Reduced Optimization. CoRR abs/2106.03056 (2021) - [i6]Laurent Condat:
Tikhonov Regularization of Circle-Valued Signals. CoRR abs/2108.02602 (2021) - 2020
- [j25]Laurent Condat:
Atomic norm minimization for decomposition into complex exponentials and optimal transport in Fourier domain. J. Approx. Theory 258: 105456 (2020) - [j24]David Orive-Miguel, Laura Di Sieno
, Anurag Behera, Edoardo Ferocino
, Davide Contini, Laurent Condat
, Lionel Hervé
, Jérôme I. Mars
, Alessandro Torricelli
, Antonio Pifferi
, Alberto Dalla Mora
:
Real-Time Dual-Wavelength Time-Resolved Diffuse Optical Tomography System for Functional Brain Imaging Based on Probe-Hosted Silicon Photomultipliers. Sensors 20(10): 2815 (2020) - [j23]Marion Foare, Nelly Pustelnik, Laurent Condat
:
Semi-Linearized Proximal Alternating Minimization for a Discrete Mumford-Shah Model. IEEE Trans. Image Process. 29: 2176-2189 (2020) - [c44]Julien Baderot, Michel Desvignes, Laurent Condat, Mauro Dalla Mura:
Tree of Shapes Cut for Material Segmentation Guided by a Design. ICASSP 2020: 2593-2597 - [c43]Grigory Malinovskiy, Dmitry Kovalev, Elnur Gasanov, Laurent Condat, Peter Richtárik:
From Local SGD to Local Fixed-Point Methods for Federated Learning. ICML 2020: 6692-6701 - [i5]Grigory Malinovsky, Dmitry Kovalev
, Elnur Gasanov, Laurent Condat
, Peter Richtárik
:
From Local SGD to Local Fixed Point Methods for Federated Learning. CoRR abs/2004.01442 (2020) - [i4]Adil Salim, Laurent Condat
, Konstantin Mishchenko
, Peter Richtárik
:
Dualize, Split, Randomize: Fast Nonsmooth Optimization Algorithms. CoRR abs/2004.02635 (2020) - [i3]Laurent Condat, Grigory Malinovsky, Peter Richtárik
:
Distributed Proximal Splitting Algorithms with Rates and Acceleration. CoRR abs/2010.00952 (2020) - [i2]Alyazeed Albasyoni, Mher Safaryan
, Laurent Condat, Peter Richtárik
:
Optimal Gradient Compression for Distributed and Federated Learning. CoRR abs/2010.03246 (2020)
2010 – 2019
- 2019
- [j22]Kevin Polisano, Laurent Condat
, Marianne Clausel, Valérie Perrier:
A Convex Approach to Superresolution and Regularization of Lines in Images. SIAM J. Imaging Sci. 12(1): 211-258 (2019) - [c42]Laurent Condat
, Daichi Kitahara, Akira Hirabayashi:
A Convex Lifting Approach to Image Phase Unwrapping. ICASSP 2019: 1852-1856 - [c41]Daichi Kitahara, Laurent Condat
, Akira Hirabayashi:
One-dimensional Edge-preserving Spline Smoothing for Estimation of Piecewise Smooth Functions. ICASSP 2019: 5611-5615 - 2018
- [j21]Franck Iutzeler
, Laurent Condat
:
Distributed Projection on the Simplex and ℓ1 Ball via ADMM and Gossip. IEEE Signal Process. Lett. 25(11): 1650-1654 (2018) - [c40]Marion Foare, Nelly Pustelnik, Laurent Condat
:
A New Proximal Method for Joint Image Restoration and Edge Detection with the Mumford-Shah Model. ICASSP 2018: 1553-1557 - [c39]Daniele Picone
, Laurent Condat
, Florian Cotte, Mauro Dalla Mura
:
Image Fusion and Reconstruction of Compressed Data: A Joint Approach. ICIP 2018: 878-882 - 2017
- [j20]Laurent Condat
:
Discrete Total Variation: New Definition and Minimization. SIAM J. Imaging Sci. 10(3): 1258-1290 (2017) - [j19]Nelly Pustelnik, Laurent Condat
:
Proximity Operator of a Sum of Functions; Application to Depth Map Estimation. IEEE Signal Process. Lett. 24(12): 1827-1831 (2017) - [c38]Laurent Condat
:
A Convex Approach to K-Means Clustering and Image Segmentation. EMMCVPR 2017: 220-234 - [c37]Alexandre Tiard, Laurent Condat
, Lucas Drumetz
, Jocelyn Chanussot, Wotao Yin, Xiaoxiang Zhu:
Robust linear unmixing with enhanced sparsity. ICIP 2017: 3140-3144 - [c36]Paolo Addesso
, Mauro Dalla Mura
, Laurent Condat
, Rocco Restaino, Gemine Vivone, Daniele Picone
, Jocelyn Chanussot:
Hyperspectral image inpainting based on collaborative total variation. ICIP 2017: 4282-4286 - [c35]Paolo Addesso
, Mauro Dalla Mura
, Laurent Condat
, Rocco Restaino, Gemine Vivone, Daniele Picone
, Jocelyn Chanussot:
Collaborative total variation for hyperspectral pansharpening. IGARSS 2017: 2597-2600 - 2016
- [j18]Laurent Condat
:
Fast projection onto the simplex and the l1 ball. Math. Program. 158(1-2): 575-585 (2016) - [j17]Jordan Frécon, Nelly Pustelnik, Patrice Abry
, Laurent Condat
:
On-The-Fly Approximation of Multivariate Total Variation Minimization. IEEE Trans. Signal Process. 64(9): 2355-2364 (2016) - [c34]Kevin Polisano, Laurent Condat
, Marianne Clausel, Valérie Perrier:
Convex super-resolution detection of lines in images. EUSIPCO 2016: 336-340 - [c33]Akira Hirabayashi, Naoki Nogami, Takashi Ijiri, Laurent Condat
:
Sequential image completion for high-speed large-pixel number sensing. EUSIPCO 2016: 948-952 - [c32]Jordan Frécon, Nelly Pustelnik, Herwig Wendt
, Laurent Condat
, Patrice Abry:
Multifractal-based texture segmentation using variational procedure. IVMSP 2016: 1-5 - [c31]Paolo Addesso
, Mauro Dalla Mura
, Laurent Condat
, Rocco Restaino, Gemine Vivone, Daniele Picone
, Jocelyn Chanussot:
Hyperspectral pansharpening using convex optimization and collaborative total variation regularization. WHISPERS 2016: 1-5 - 2015
- [c30]Naoki Nogami, Akira Hirabayashi, Jeremy White, Laurent Condat:
Improvement of pixel enhancement algorithm for high-speed camera imaging using 3D sparsity. APSIPA 2015: 952-957 - [c29]Laurent Condat
, Akira Hirabayashi:
Super-resolution of positive spikes by Toeplitz low-rank approximation. EUSIPCO 2015: 459-463 - [c28]Akira Hirabayashi, Nogami Nogami, Jeremy White, Laurent Condat
:
Pixel enlargement in high-speed camera image acquisition based on 3D sparse representations. SiPS 2015: 1-6 - [i1]Jordan Frécon, Nelly Pustelnik, Patrice Abry, Laurent Condat:
On-the-fly Approximation of Multivariate Total Variation Minimization. CoRR abs/1504.05854 (2015) - 2014
- [j16]Laurent Condat
:
A Generic Proximal Algorithm for Convex Optimization - Application to Total Variation Minimization. IEEE Signal Process. Lett. 21(8): 985-989 (2014) - [j15]Xiyan He, Laurent Condat
, José M. Bioucas-Dias
, Jocelyn Chanussot, Junshi Xia:
A New Pansharpening Method Based on Spatial and Spectral Sparsity Priors. IEEE Trans. Image Process. 23(9): 4160-4174 (2014) - [j14]Jeremy Schmitt, Nelly Pustelnik, Pierre Borgnat
, Patrick Flandrin, Laurent Condat
:
2D Prony-Huang Transform: A New Tool for 2D Spectral Analysis. IEEE Trans. Image Process. 23(12): 5233-5248 (2014) - [c27]Laurent Condat:
Semi-local total variation for regularization of inverse problems. EUSIPCO 2014: 1806-1810 - [c26]Patrick L. Combettes
, Laurent Condat
, Jean-Christophe Pesquet, Bang Công Vu:
A forward-backward view of some primal-dual optimization methods in image recovery. ICIP 2014: 4141-4145 - [c25]Kevin Polisano, Marianne Clausel, Valérie Perrier, Laurent Condat
:
Texture modeling by Gaussian fields with prescribed local orientation. ICIP 2014: 6091-6095 - [c24]Laurent Condat, Jérôme Boulanger, Nelly Pustelnik, Souleymen Sahnoun, Lucie Sengmanivong:
A 2-D spectral analysis method to estimate the modulation parameters in structured illumination microscopy. ISBI 2014: 604-607 - [c23]Jérôme Boulanger, Nelly Pustelnik, Laurent Condat:
Non-smooth convex optimization for an efficient reconstruction in structured illumination microscopy. ISBI 2014: 995-998 - 2013
- [j13]Laurent Condat
:
Reconstruction from non-uniform samples: A direct, variational approach in shift-invariant spaces. Digit. Signal Process. 23(4): 1277-1287 (2013) - [j12]Akira Hirabayashi, Yosuke Hironaga, Laurent Condat
:
Sampling Signals with Finite Rate of Innovation and Recovery by Maximum Likelihood Estimation. IEICE Trans. Fundam. Electron. Commun. Comput. Sci. 96-A(10): 1972-1979 (2013) - [j11]Laurent Condat
:
A Primal-Dual Splitting Method for Convex Optimization Involving Lipschitzian, Proximable and Linear Composite Terms. J. Optim. Theory Appl. 158(2): 460-479 (2013) - [j10]Laurent Condat
:
A Direct Algorithm for 1-D Total Variation Denoising. IEEE Signal Process. Lett. 20(11): 1054-1057 (2013) - [c22]Laurent Condat
, Akira Hirabayashi, Yosuke Hironaga:
Recovery of nonuniformdirac pulses from noisy linear measurements. ICASSP 2013: 6014-6018 - [c21]Akira Hirabayashi, Yosuke Hironaga, Laurent Condat
:
Sampling and recovery of continuous sparse signals by maximum likelihood estimation. ICASSP 2013: 6058-6062 - 2012
- [j9]Giorgio Licciardi, Muhammad Murtaza Khan
, Jocelyn Chanussot, Annick Montanvert, Laurent Condat
, Christian Jutten:
Fusion of hyperspectral and panchromatic images using multiresolution analysis and nonlinear PCA band reduction. EURASIP J. Adv. Signal Process. 2012: 207 (2012) - [c20]Laurent Condat
, Saleh Mosaddegh:
Joint demosaicking and denoising by total variationminimization. ICIP 2012: 2781-2784 - [c19]Xiyan He, Laurent Condat
, Jocelyn Chanussot, Junshi Xia:
Pansharpening using total variation regularization. IGARSS 2012: 166-169 - 2011
- [j8]Laurent Condat
:
A New Color Filter Array With Optimal Properties for Noiseless and Noisy Color Image Acquisition. IEEE Trans. Image Process. 20(8): 2200-2210 (2011) - [j7]Laurent Condat
, Torsten Möller
:
Quantitative Error Analysis for the Reconstruction of Derivatives. IEEE Trans. Signal Process. 59(6): 2965-2969 (2011) - [c18]Laurent Condat:
Reconstruction of derivatives: Error analysis and design criteria. EUSIPCO 2011: 839-843 - [c17]Giorgio Licciardi, Muhammad Murtaza Khan
, Jocelyn Chanussot, Annick Montanvert, Laurent Condat
, Christian Jutten:
Fusion of Hyperspectral and panchromatic images using multiresolution analysis and nonlinear PCA band reduction. IGARSS 2011: 1783-1786 - 2010
- [j6]Laurent Condat
:
Color filter array design using random patterns with blue noise chromatic spectra. Image Vis. Comput. 28(8): 1196-1202 (2010) - [j5]Usman R. Alim, Torsten Möller
, Laurent Condat
:
Gradient Estimation Revitalized. IEEE Trans. Vis. Comput. Graph. 16(6): 1495-1504 (2010) - [c16]Laurent Condat
:
A simple, fast and efficient approach to denoisaicking: Joint demosaicking and denoising. ICIP 2010: 905-908
2000 – 2009
- 2009
- [c15]Laurent Condat
:
A new color filter array with optimal sensing properties. ICIP 2009: 457-460 - [c14]Laurent Condat
:
A new random color filter array with good spectral properties. ICIP 2009: 1613-1616 - [c13]Laurent Condat
:
A generic variational approach for demosaicking from an arbitrary color filter array. ICIP 2009: 1625-1628 - 2008
- [j4]Muhammad Murtaza Khan
, Jocelyn Chanussot, Laurent Condat
, Annick Montanvert:
Indusion: Fusion of Multispectral and Panchromatic Images Using the Induction Scaling Technique. IEEE Geosci. Remote. Sens. Lett. 5(1): 98-102 (2008) - [j3]Laurent Condat
, Dimitri Van De Ville
, Brigitte Forster-Heinlein
:
Reversible, Fast, and High-Quality Grid Conversions. IEEE Trans. Image Process. 17(5): 679-693 (2008) - [c12]Akira Hirabayashi, Laurent Condat:
A study on interlaced sampling with unknown offsets. EUSIPCO 2008: 1-5 - [c11]Laurent Condat
, Dimitri Van De Ville
:
Fully reversible image rotation by 1-D filtering. ICIP 2008: 913-916 - [c10]Laurent Condat
, Dimitri Van De Ville
:
New optimized spline functions for interpolation on the hexagonal lattice. ICIP 2008: 1256-1259 - 2007
- [j2]Laurent Condat
, Dimitri Van De Ville
:
Quasi-Interpolating Spline Models for Hexagonally-Sampled Data. IEEE Trans. Image Process. 16(5): 1195-1206 (2007) - [c9]Akira Hirabayashi, Laurent Condat:
Torwards a general formulation for over-sampling and under-sampling. EUSIPCO 2007: 1985-1989 - [c8]Laurent Condat
, Brigitte Forster-Heinlein
, Dimitri Van De Ville:
H2O: Reversible Hexagonal-Orthogonal Grid Conversion by 1-D Filtering. ICIP (2) 2007: 73-76 - [c7]Akira Hirabayashi, Laurent Condat
:
A Compact Image Magnification Method with Preservation of Preferential Components. ICIP (5) 2007: 385-388 - [c6]Muhammad Murtaza Khan
, Jocelyn Chanussot, Annick Montanvert, Laurent Condat
:
Pan-sharpening using induction. IGARSS 2007: 314-317 - 2006
- [b1]Laurent Condat:
Méthodes d'approximation pour la reconstruction de signaux et le redimensionnement d'images. (Approximation methods for signal reconstruction and image resizing). Grenoble Institute of Technology, France, 2006 - [j1]Laurent Condat
, Dimitri Van De Ville
:
Three-directional box-splines: characterization and efficient evaluation. IEEE Signal Process. Lett. 13(7): 417-420 (2006) - [c5]Laurent Condat, Annick Montanvert:
Fast reconstruction from non-uniform samples in shift-invariant spaces. EUSIPCO 2006: 1-5 - [c4]Laurent Condat
, Dimitri Van De Ville
, Michael Unser
:
Efficient Reconstruction of Hexagonally Sampled Data using Three-Directional Box-Splines. ICIP 2006: 697-700 - 2005
- [c3]Laurent Condat
, Annick Montanvert:
A Framework for Image Magnification: Induction Revisited. ICASSP (2) 2005: 845-848 - [c2]Laurent Condat
, Thierry Blu, Michael Unser
:
Beyond interpolation: optimal reconstruction by quasi-interpolation. ICIP (1) 2005: 33-36 - [c1]Laurent Condat
, Dimitri Van De Ville
, Thierry Blu:
Hexagonal versus orthogonal lattices: a new comparison using approximation theory. ICIP (3) 2005: 1116-1119
Coauthor Index
![](https://dblp.uni-trier.de/img/cog.dark.24x24.png)
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
Unpaywalled article links
Add open access links from to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from ,
, and
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and
to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from .
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2024-07-25 20:24 CEST by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint