Kevin Scaman

Publication Activity (10 Years)

Years Active: 2014-2024
Publications (10 Years): 35

Top Topics

Diffusion Processes

Stochastic Gradient Descent

Top Venues

Publications

Kevin Scaman, Mathieu Even, Batiste Le Bars, Laurent Massoulié
Minimax Excess Risk of First-Order Methods for Statistical Learning with Data-Dependent Oracles. AISTATS (2024)
David A. R. Robin, Kevin Scaman, Marc Lelarge
Random Sparse Lifts: Construction, Analysis and Convergence of finite sparse networks. ICLR (2024)
Yann Fraboni, Martin Van Waerebeke, Kevin Scaman, Richard Vidal, Laetitia Kameni, Marco Lorenzi
SIFU: Sequential Informed Federated Unlearning for Efficient and Provable Client Unlearning in Federated Optimization. AISTATS (2024)
David A. R. Robin, Kevin Scaman, Marc Lelarge
Convergence beyond the over-parameterized regime using Rayleigh quotients. CoRR (2023)
Kevin Scaman, Mathieu Even, Laurent Massoulié
Generalization Error of First-Order Methods for Statistical Learning with Generic Oracles. CoRR (2023)
Kevin Scaman
Breaking the Log Barrier: a Novel Universal Restart Strategy for Faster Las Vegas Algorithms. CoRR (2023)
David A. R. Robin, Kevin Scaman, Marc Lelarge
Periodic signal recovery with regularized sine neural networks. NeurReps (2022)
Cédric Malherbe, Kevin Scaman
Robustness in Multi-Objective Submodular Optimization: a Quantile Approach. ICML (2022)
Kevin Scaman, Cédric Malherbe, Ludovic Dos Santos
Convergence Rates of Non-Convex Stochastic Gradient Descent Under a Generic Lojasiewicz Condition and Local Smoothness. ICML (2022)
David A. R. Robin, Kevin Scaman, Marc Lelarge
Convergence beyond the over-parameterized regime using Rayleigh quotients. NeurIPS (2022)
Mathieu Even, Laurent Massoulié, Kevin Scaman
On Sample Optimality in Personalized Collaborative and Federated Learning. NeurIPS (2022)
Alain Durmus, Eric Moulines, Alexey Naumov, Sergey Samsonov, Kevin Scaman, Hoi-To Wai
Tight High Probability Bounds for Linear Stochastic Approximation with Fixed Stepsize. NeurIPS (2021)
George Dasoulas, Giannis Nikolentzos, Kevin Scaman, Aladin Virmaux, Michalis Vazirgiannis
Ego-Based Entropy Measures for Structural Representations on Graphs. ICASSP (2021)
Avery Ma, Aladin Virmaux, Kevin Scaman, Juwei Lu
Improving Hierarchical Adversarial Robustness of Deep Neural Networks. CoRR (2021)
George Dasoulas, Giannis Nikolentzos, Kevin Scaman, Aladin Virmaux, Michalis Vazirgiannis
Ego-based Entropy Measures for Structural Representations on Graphs. CoRR (2021)
Alain Durmus, Eric Moulines, Alexey Naumov, Sergey Samsonov, Kevin Scaman, Hoi-To Wai
Tight High Probability Bounds for Linear Stochastic Approximation with Fixed Stepsize. CoRR (2021)
George Dasoulas, Kevin Scaman, Aladin Virmaux
Lipschitz Normalization for Self-Attention Layers with Application to Graph Neural Networks. CoRR (2021)
George Dasoulas, Kevin Scaman, Aladin Virmaux
Lipschitz normalization for self-attention layers with application to graph neural networks. ICML (2021)
Kevin Scaman, Cédric Malherbe
Robustness Analysis of Non-Convex Stochastic Gradient Descent using Biased Expectations. NeurIPS (2020)
George Dasoulas, Giannis Nikolentzos, Kevin Scaman, Aladin Virmaux, Michalis Vazirgiannis
Ego-based Entropy Measures for Structural Representations. CoRR (2020)
George Dasoulas, Ludovic Dos Santos, Kevin Scaman, Aladin Virmaux
Coloring Graph Neural Networks for Node Disambiguation. IJCAI (2020)
Kevin Scaman, Ludovic Dos Santos, Merwan Barlier, Igor Colin
A Simple and Efficient Smoothing Method for Faster Optimization and Local Exploration. NeurIPS (2020)
Kevin Scaman, Francis R. Bach, Sébastien Bubeck, Yin Tat Lee, Laurent Massoulié
Optimal Convergence Rates for Convex Distributed Optimization in Networks. J. Mach. Learn. Res. 20 (2019)
Igor Colin, Ludovic Dos Santos, Kevin Scaman
Theoretical Limits of Pipeline Parallel Optimization and Application to Distributed Deep Learning. NeurIPS (2019)
George Dasoulas, Ludovic Dos Santos, Kevin Scaman, Aladin Virmaux
Coloring graph neural networks for node disambiguation. CoRR (2019)
Igor Colin, Ludovic Dos Santos, Kevin Scaman
Theoretical Limits of Pipeline Parallel Optimization and Application to Distributed Deep Learning. CoRR (2019)
Moez Draief, Konstantin Kutzkov, Kevin Scaman, Milan Vojnovic
KONG: Kernels for ordered-neighborhood graphs. NeurIPS (2018)
Kevin Scaman, Aladin Virmaux
Lipschitz regularity of deep neural networks: analysis and efficient estimation. CoRR (2018)
Moez Draief, Konstantin Kutzkov, Kevin Scaman, Milan Vojnovic
KONG: Kernels for ordered-neighborhood graphs. CoRR (2018)
Kevin Scaman, Francis R. Bach, Sébastien Bubeck, Laurent Massoulié, Yin Tat Lee
Optimal Algorithms for Non-Smooth Distributed Optimization in Networks. NeurIPS (2018)
Aladin Virmaux, Kevin Scaman
Lipschitz regularity of deep neural networks: analysis and efficient estimation. NeurIPS (2018)
Kevin Scaman, Argyris Kalogeratos, Luca Corinzia, Nicolas Vayatis
A Spectral Method for Activity Shaping in Continuous-Time Information Cascades. CoRR (2017)
Rémi Lemonnier, Kevin Scaman, Argyris Kalogeratos
Multivariate Hawkes Processes for Large-Scale Inference. AAAI (2017)
Kevin Scaman, Francis R. Bach, Sébastien Bubeck, Yin Tat Lee, Laurent Massoulié
Optimal Algorithms for Smooth and Strongly Convex Distributed Optimization in Networks. ICML (2017)
Kevin Scaman, Argyris Kalogeratos, Nicolas Vayatis
Suppressing Epidemics in Networks Using Priority Planning. IEEE Trans. Netw. Sci. Eng. 3 (4) (2016)
Kevin Scaman, Rémi Lemonnier, Nicolas Vayatis
Anytime Influence Bounds and the Explosive Behavior of Continuous-Time Diffusion Networks. NIPS (2015)
Kevin Scaman, Argyris Kalogeratos, Nicolas Vayatis
A Greedy Approach for Dynamic Control of Diffusion Processes in Networks. ICTAI (2015)
Kevin Scaman, Argyris Kalogeratos, Nicolas Vayatis
What Makes a Good Plan? An Efficient Planning Approach to Control Diffusion Processes in Networks. CoRR (2014)
Rémi Lemonnier, Kevin Scaman, Nicolas Vayatis
Tight Bounds for Influence in Diffusion Networks and Application to Bond Percolation and Epidemiology. NIPS (2014)
Rémi Lemonnier, Kevin Scaman, Nicolas Vayatis
Tight Bounds for Influence in Diffusion Networks and Application to Bond Percolation and Epidemiology. CoRR (2014)