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Laurent Condat
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- affiliation: Grenoble Institute of Technology, France
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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
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
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
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
From Local SGD to Local Fixed Point Methods for Federated Learning. CoRR abs/2004.01442 (2020) - [i4]Adil Salim, Laurent Condat
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
Optimal Gradient Compression for Distributed and Federated Learning. CoRR abs/2010.03246 (2020)
2010 – 2019
- 2019
- [j22]Kevin Polisano, Laurent Condat
A Convex Approach to Superresolution and Regularization of Lines in Images. SIAM J. Imaging Sci. 12(1): 211-258 (2019) - [c42]Laurent Condat
A Convex Lifting Approach to Image Phase Unwrapping. ICASSP 2019: 1852-1856 - [c41]Daichi Kitahara, Laurent Condat
One-dimensional Edge-preserving Spline Smoothing for Estimation of Piecewise Smooth Functions. ICASSP 2019: 5611-5615 - 2018
- [j21]Franck Iutzeler
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
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
Robust linear unmixing with enhanced sparsity. ICIP 2017: 3140-3144 - [c36]Paolo Addesso
Hyperspectral image inpainting based on collaborative total variation. ICIP 2017: 4282-4286 - [c35]Paolo Addesso
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
On-The-Fly Approximation of Multivariate Total Variation Minimization. IEEE Trans. Signal Process. 64(9): 2355-2364 (2016) - [c34]Kevin Polisano, Laurent Condat
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
Multifractal-based texture segmentation using variational procedure. IVMSP 2016: 1-5 - [c31]Paolo Addesso
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
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
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
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
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
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
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
Joint demosaicking and denoising by total variationminimization. ICIP 2012: 2781-2784 - [c19]Xiyan He, Laurent Condat
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
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
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
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
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
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
Fully reversible image rotation by 1-D filtering. ICIP 2008: 913-916 - [c10]Laurent Condat
New optimized spline functions for interpolation on the hexagonal lattice. ICIP 2008: 1256-1259 - 2007
- [j2]Laurent Condat
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
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
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
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
Efficient Reconstruction of Hexagonally Sampled Data using Three-Directional Box-Splines. ICIP 2006: 697-700 - 2005
- [c3]Laurent Condat
A Framework for Image Magnification: Induction Revisited. ICASSP (2) 2005: 845-848 - [c2]Laurent Condat
Beyond interpolation: optimal reconstruction by quasi-interpolation. ICIP (1) 2005: 33-36 - [c1]Laurent Condat
Hexagonal versus orthogonal lattices: a new comparison using approximation theory. ICIP (3) 2005: 1116-1119
Coauthor Index
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last updated on 2024-07-25 20:24 CEST by the dblp team
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