Scaman, Kevin

26 publications

AAAI 2025 In-Depth Analysis of Low-Rank Matrix Factorisation in a Federated Setting Constantin Philippenko, Kevin Scaman, Laurent Massoulié

NeurIPS 2025 Stab-SGD: Noise-Adaptivity in Smooth Optimization with Stability Ratios David A. R. Robin, Killian Bakong, Kevin Scaman

ICML 2025 When to Forget? Complexity Trade-Offs in Machine Unlearning Martin Van Waerebeke, Marco Lorenzi, Giovanni Neglia, Kevin Scaman

ICML 2024 Improved Stability and Generalization Guarantees of the Decentralized SGD Algorithm Batiste Le Bars, Aurélien Bellet, Marc Tommasi, Kevin Scaman, Giovanni Neglia

AISTATS 2024 Minimax Excess Risk of First-Order Methods for Statistical Learning with Data-Dependent Oracles Kevin Scaman, Mathieu Even, Batiste Le Bars, Laurent Massoulie

ICLR 2024 Random Sparse Lifts: Construction, Analysis and Convergence of Finite Sparse Networks David A. R. Robin, Kevin Scaman, Marc Lelarge

AISTATS 2024 SIFU: Sequential Informed Federated Unlearning for Efficient and Provable Client Unlearning in Federated Optimization Yann Fraboni, Martin Van Waerebeke, Kevin Scaman, Richard Vidal, Laetitia Kameni, Marco Lorenzi

NeurIPS 2022 Convergence Beyond the Over-Parameterized Regime Using Rayleigh Quotients David A. R. Robin, Kevin Scaman, Marc Lelarge

ICML 2022 Convergence Rates of Non-Convex Stochastic Gradient Descent Under a Generic Lojasiewicz Condition and Local Smoothness Kevin Scaman, Cedric Malherbe, Ludovic Dos Santos

NeurIPS 2022 On Sample Optimality in Personalized Collaborative and Federated Learning Mathieu Even, Laurent Massoulié, Kevin Scaman

NeurIPSW 2022 Periodic Signal Recovery with Regularized Sine Neural Networks David A. R. Robin, Kevin Scaman, Marc Lelarge

ICML 2022 Robustness in Multi-Objective Submodular Optimization: A Quantile Approach Cedric Malherbe, Kevin Scaman

ICML 2021 Lipschitz Normalization for Self-Attention Layers with Application to Graph Neural Networks George Dasoulas, Kevin Scaman, Aladin Virmaux

NeurIPS 2021 Tight High Probability Bounds for Linear Stochastic Approximation with Fixed Stepsize Alain Durmus, Eric Moulines, Alexey Naumov, Sergey Samsonov, Kevin Scaman, Hoi-To Wai

NeurIPS 2020 A Simple and Efficient Smoothing Method for Faster Optimization and Local Exploration Kevin Scaman, Ludovic Dos Santos, Merwan Barlier, Igor Colin

IJCAI 2020 Coloring Graph Neural Networks for Node Disambiguation George Dasoulas, Ludovic Dos Santos, Kevin Scaman, Aladin Virmaux

NeurIPS 2020 Robustness Analysis of Non-Convex Stochastic Gradient Descent Using Biased Expectations Kevin Scaman, Cedric Malherbe

JMLR 2019 Optimal Convergence Rates for Convex Distributed Optimization in Networks Kevin Scaman, Francis Bach, Sébastien Bubeck, Yin Tat Lee, Laurent Massoulié

NeurIPS 2019 Theoretical Limits of Pipeline Parallel Optimization and Application to Distributed Deep Learning Igor Colin, Ludovic Dos Santos, Kevin Scaman

NeurIPS 2018 KONG: Kernels for Ordered-Neighborhood Graphs Moez Draief, Konstantin Kutzkov, Kevin Scaman, Milan Vojnovic

NeurIPS 2018 Lipschitz Regularity of Deep Neural Networks: Analysis and Efficient Estimation Aladin Virmaux, Kevin Scaman

NeurIPS 2018 Optimal Algorithms for Non-Smooth Distributed Optimization in Networks Kevin Scaman, Francis Bach, Sebastien Bubeck, Laurent Massoulié, Yin Tat Lee

AAAI 2017 Multivariate Hawkes Processes for Large-Scale Inference Rémi Lemonnier, Kevin Scaman, Argyris Kalogeratos

ICML 2017 Optimal Algorithms for Smooth and Strongly Convex Distributed Optimization in Networks Kevin Scaman, Francis Bach, Sébastien Bubeck, Yin Tat Lee, Laurent Massoulié

NeurIPS 2015 Anytime Influence Bounds and the Explosive Behavior of Continuous-Time Diffusion Networks Kevin Scaman, Rémi Lemonnier, Nicolas Vayatis

NeurIPS 2014 Tight Bounds for Influence in Diffusion Networks and Application to Bond Percolation and Epidemiology Remi Lemonnier, Kevin Scaman, Nicolas Vayatis