<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>1 | Mathieu Even</title><link>https://mathieueven.netlify.app/publication-type/1/</link><atom:link href="https://mathieueven.netlify.app/publication-type/1/index.xml" rel="self" type="application/rss+xml"/><description>1</description><generator>Wowchemy (https://wowchemy.com)</generator><language>en-us</language><lastBuildDate>Sun, 25 May 2025 08:12:10 +0000</lastBuildDate><image><url>https://mathieueven.netlify.app/media/icon_hu84e8a831218901ac0ed107b670309c14_16617_512x512_fill_lanczos_center_3.png</url><title>1</title><link>https://mathieueven.netlify.app/publication-type/1/</link></image><item><title>Model Agnostic Differentially Private Causal Inference</title><link>https://mathieueven.netlify.app/publication/dp-causal-ate/</link><pubDate>Sun, 25 May 2025 08:12:10 +0000</pubDate><guid>https://mathieueven.netlify.app/publication/dp-causal-ate/</guid><description/></item><item><title>Rethinking the Win Ratio A Causal Framework for Hierarchical Outcome Analysis</title><link>https://mathieueven.netlify.app/publication/winratio/</link><pubDate>Sun, 02 Feb 2025 08:12:10 +0000</pubDate><guid>https://mathieueven.netlify.app/publication/winratio/</guid><description/></item><item><title>Long-Context Linear System Identification</title><link>https://mathieueven.netlify.app/publication/ldscontext/</link><pubDate>Sat, 02 Nov 2024 08:12:10 +0000</pubDate><guid>https://mathieueven.netlify.app/publication/ldscontext/</guid><description/></item><item><title>Asynchronous speedup in decentralized optimization</title><link>https://mathieueven.netlify.app/publication/asynchrony-and-acceleration-in-gossip-algorithms/</link><pubDate>Tue, 10 Sep 2024 09:47:10 +0000</pubDate><guid>https://mathieueven.netlify.app/publication/asynchrony-and-acceleration-in-gossip-algorithms/</guid><description/></item><item><title>Asynchronous SGD on Graphs - a Unified Framework for Asynchronous Decentralized and Federated Optimization</title><link>https://mathieueven.netlify.app/publication/dasgd/</link><pubDate>Fri, 02 Feb 2024 08:12:10 +0000</pubDate><guid>https://mathieueven.netlify.app/publication/dasgd/</guid><description/></item><item><title>Aligning Embeddings and Geometric Random Graphs, Informational Results and Computational Approaches for the Procrustes-Wasserstein Problem</title><link>https://mathieueven.netlify.app/publication/pw/</link><pubDate>Thu, 02 Feb 2023 08:12:10 +0000</pubDate><guid>https://mathieueven.netlify.app/publication/pw/</guid><description/></item><item><title>Implicit Regularisation, Large Stepsizes and Edge of Stability for (S)GD over Diagonal Linear Networks</title><link>https://mathieueven.netlify.app/publication/sgd-over-diagonal-linear-networks/</link><pubDate>Thu, 02 Feb 2023 08:12:10 +0000</pubDate><guid>https://mathieueven.netlify.app/publication/sgd-over-diagonal-linear-networks/</guid><description/></item><item><title>Stochastic Gradient Descent under Markov Chain Sampling Schemes</title><link>https://mathieueven.netlify.app/publication/mcsgd/</link><pubDate>Mon, 02 Jan 2023 08:12:10 +0000</pubDate><guid>https://mathieueven.netlify.app/publication/mcsgd/</guid><description/></item><item><title>Asynchronous SGD Beats Minibatch SGD Under Arbitrary Delays</title><link>https://mathieueven.netlify.app/publication/asynchronous-sgd-beats-minibatch-sgd-under-arbitrary-delays/</link><pubDate>Fri, 08 Jul 2022 08:12:10 +0000</pubDate><guid>https://mathieueven.netlify.app/publication/asynchronous-sgd-beats-minibatch-sgd-under-arbitrary-delays/</guid><description/></item><item><title>Muffliato Peer-to-Peer Privacy Amplification for Decentralized Optimization and Averaging</title><link>https://mathieueven.netlify.app/publication/muffliato/</link><pubDate>Fri, 08 Jul 2022 08:12:10 +0000</pubDate><guid>https://mathieueven.netlify.app/publication/muffliato/</guid><description/></item><item><title>On Sample Optimality in Personalized Federated and Collaborative Learning</title><link>https://mathieueven.netlify.app/publication/sample-optimality-and-all-for-all-strategies-in-personalized-federated-and-collaborative-learning/</link><pubDate>Mon, 10 Jan 2022 09:47:10 +0000</pubDate><guid>https://mathieueven.netlify.app/publication/sample-optimality-and-all-for-all-strategies-in-personalized-federated-and-collaborative-learning/</guid><description>&lt;p>[See on arxiv](&lt;a href="https://arxiv.org/pdf/2201.13097.pdf%29[n" target="_blank" rel="noopener">https://arxiv.org/pdf/2201.13097.pdf)[n&lt;/a> arxiv](&lt;a href="https://arxiv.org/pdf/2201.13097.pdf" target="_blank" rel="noopener">https://arxiv.org/pdf/2201.13097.pdf&lt;/a>)&lt;/p></description></item><item><title>A Continuized View on Nesterov Acceleration for Stochastic Gradient Descent and Randomized Gossip</title><link>https://mathieueven.netlify.app/publication/a-continuized-view-on-nesterov-acceleration-for-stochastic-gradient-descent-and-randomized-gossip/</link><pubDate>Fri, 10 Sep 2021 14:49:12 +0000</pubDate><guid>https://mathieueven.netlify.app/publication/a-continuized-view-on-nesterov-acceleration-for-stochastic-gradient-descent-and-randomized-gossip/</guid><description/></item><item><title>Fast Stochastic Bregman Gradient Methods: Sharp Analysis and Variance Reduction</title><link>https://mathieueven.netlify.app/publication/fast-stochastic-bregman-gradient-methods-sharp-analysis-and-variance-reduction/</link><pubDate>Fri, 10 Sep 2021 14:47:04 +0000</pubDate><guid>https://mathieueven.netlify.app/publication/fast-stochastic-bregman-gradient-methods-sharp-analysis-and-variance-reduction/</guid><description/></item><item><title>Concentration of Non-Isotropic Random Tensors with Applications to Learning and Empirical Risk Minimization</title><link>https://mathieueven.netlify.app/publication/concentration-of-non-isotropic-random-tensors-with-applications-to-learning-and-empirical-risk-minimization/</link><pubDate>Fri, 10 Sep 2021 14:45:24 +0000</pubDate><guid>https://mathieueven.netlify.app/publication/concentration-of-non-isotropic-random-tensors-with-applications-to-learning-and-empirical-risk-minimization/</guid><description/></item></channel></rss>