About Laurent Condat Laurent Condat Senior Research Scientist, Computer Science optimization Federated learning Distributed algorithms Signal and Image Processing I am a senior researcher working in optimization, in the group of Prof. Peter Richtárik. Events Presented Events Oct 26 - Nov 1, 2025 BiCoLoR: Communication-Efficient Optimization with Bidirectional Compression and Local Training Laurent Condat, Senior Research Scientist, Computer Science Oct 27, 12:00 - 13:00 B9 L2 R2325 optimization Distributed algorithms Signal and Image Processing We introduce BiCoLoR, the first algorithm to combine local training with bidirectional compression using arbitrary unbiased compressors, achieving accelerated complexity and demonstrating superior empirical performance. Nov 24 - Nov 30, 2024 LoCoDL: Communication-Efficient Distributed Optimization with Local Training and Compression Laurent Condat, Senior Research Scientist, Computer Science Nov 27, 12:00 - 13:00 B9, L3, R3125 In distributed optimization, and even more in federated learning, communication is the main bottleneck. We introduce LoCoDL, a communication-efficient algorithm that leverages the two techniques of Local training, which reduces the communication frequency, and Compression with a large class of unbiased compressors that includes sparsification and quantization strategies. Mar 19 - Mar 25, 2023 Proximal Algorithms for Large-Scale Convex Non-smooth Optimization Laurent Condat, Senior Research Scientist, Computer Science Mar 21, 16:00 - 17:00 B2 L5 R5220 algorithms stochastic algorithm Convex nonsmooth optimization problems, whose solutions live in very high dimensional spaces, have become ubiquitous. To solve them, the class of iterative fixed-point algorithms known as proximal splitting algorithms is particularly adequate: they consist of simple operations, handling the terms in the objective function separately. I will present a selection of recent primal-dual algorithms within a unified framework, which consists in solving monotone inclusions with well-chosen spaces and metrics. Oct 9 - Oct 15, 2022 EF-BV: distributed optimization with compressed communication Laurent Condat, Senior Research Scientist, Computer Science Oct 10, 12:00 - 13:00 B9 L2 R2322 H1 big data distributed computing Federated learning In the big data era, it is necessary to rely on distributed computing. For distributed optimization and learning tasks, in particular in the modern paradigm of federated learning, specific challenges arise, such as decentralized data storage. Communication between the parallel machines and the orchestrating distant server is necessary but slow. To address this main bottleneck, a natural strategy is to compress the communicated vectors. I will present EF-BV, a new algorithm which converges linearly to an exact solution, with a large class of deterministic or random, biased or unbiased compressors. Dec 1 - Dec 7, 2019 Proximal Splitting Methods for Convex Optimization: An Introduction Laurent Condat, Senior Research Scientist, Computer Science Dec 2, 12:00 - 13:00 B9 L2 H1 R2322 This talk will be a gentle introduction to proximal splitting algorithms to minimize a sum of possibly nonsmooth convex functions. Several such algorithms date back to the 60s, but the last 10 years have seen the development of new primal-dual splitting algorithms, motivated by the need to solve large-scale problems in signal and image processing, machine learning, and more generally data science. No background will be necessary to attend the talk, whose goal is to present the intuitions behind this class of methods.
BiCoLoR: Communication-Efficient Optimization with Bidirectional Compression and Local Training Laurent Condat, Senior Research Scientist, Computer Science Oct 27, 12:00 - 13:00 B9 L2 R2325 optimization Distributed algorithms Signal and Image Processing We introduce BiCoLoR, the first algorithm to combine local training with bidirectional compression using arbitrary unbiased compressors, achieving accelerated complexity and demonstrating superior empirical performance.
LoCoDL: Communication-Efficient Distributed Optimization with Local Training and Compression Laurent Condat, Senior Research Scientist, Computer Science Nov 27, 12:00 - 13:00 B9, L3, R3125 In distributed optimization, and even more in federated learning, communication is the main bottleneck. We introduce LoCoDL, a communication-efficient algorithm that leverages the two techniques of Local training, which reduces the communication frequency, and Compression with a large class of unbiased compressors that includes sparsification and quantization strategies.
Proximal Algorithms for Large-Scale Convex Non-smooth Optimization Laurent Condat, Senior Research Scientist, Computer Science Mar 21, 16:00 - 17:00 B2 L5 R5220 algorithms stochastic algorithm Convex nonsmooth optimization problems, whose solutions live in very high dimensional spaces, have become ubiquitous. To solve them, the class of iterative fixed-point algorithms known as proximal splitting algorithms is particularly adequate: they consist of simple operations, handling the terms in the objective function separately. I will present a selection of recent primal-dual algorithms within a unified framework, which consists in solving monotone inclusions with well-chosen spaces and metrics.
EF-BV: distributed optimization with compressed communication Laurent Condat, Senior Research Scientist, Computer Science Oct 10, 12:00 - 13:00 B9 L2 R2322 H1 big data distributed computing Federated learning In the big data era, it is necessary to rely on distributed computing. For distributed optimization and learning tasks, in particular in the modern paradigm of federated learning, specific challenges arise, such as decentralized data storage. Communication between the parallel machines and the orchestrating distant server is necessary but slow. To address this main bottleneck, a natural strategy is to compress the communicated vectors. I will present EF-BV, a new algorithm which converges linearly to an exact solution, with a large class of deterministic or random, biased or unbiased compressors.
Proximal Splitting Methods for Convex Optimization: An Introduction Laurent Condat, Senior Research Scientist, Computer Science Dec 2, 12:00 - 13:00 B9 L2 H1 R2322 This talk will be a gentle introduction to proximal splitting algorithms to minimize a sum of possibly nonsmooth convex functions. Several such algorithms date back to the 60s, but the last 10 years have seen the development of new primal-dual splitting algorithms, motivated by the need to solve large-scale problems in signal and image processing, machine learning, and more generally data science. No background will be necessary to attend the talk, whose goal is to present the intuitions behind this class of methods.
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