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Journal Articles
- 2019
[j2]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: 159:1-159:31 (2019)- 2016
- [j1]Kevin Scaman, Argyris Kalogeratos, Nicolas Vayatis:
Suppressing Epidemics in Networks Using Priority Planning. IEEE Trans. Netw. Sci. Eng. 3(4): 271-285 (2016)
Conference and Workshop Papers
- 2024
- [c24]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: 3457-3465 - [c23]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: 3709-3717 - [c22]David A. R. Robin, Kevin Scaman, Marc Lelarge:
Random Sparse Lifts: Construction, Analysis and Convergence of finite sparse networks. ICLR 2024 - [c21]Batiste Le Bars, Aurélien Bellet, Marc Tommasi, Kevin Scaman, Giovanni Neglia:
Improved Stability and Generalization Guarantees of the Decentralized SGD Algorithm. ICML 2024 - 2022
- [c20]Cédric Malherbe, Kevin Scaman:
Robustness in Multi-Objective Submodular Optimization: a Quantile Approach. ICML 2022: 14871-14886 - [c19]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: 19310-19327 - [c18]David A. R. Robin, Kevin Scaman, Marc Lelarge:
Periodic signal recovery with regularized sine neural networks. NeurReps 2022: 98-110 - [c17]Mathieu Even, Laurent Massoulié, Kevin Scaman:
On Sample Optimality in Personalized Collaborative and Federated Learning. NeurIPS 2022 - [c16]David A. R. Robin, Kevin Scaman, Marc Lelarge:
Convergence beyond the over-parameterized regime using Rayleigh quotients. NeurIPS 2022 - 2021
- [c15]George Dasoulas, Giannis Nikolentzos, Kevin Scaman, Aladin Virmaux, Michalis Vazirgiannis:
Ego-Based Entropy Measures for Structural Representations on Graphs. ICASSP 2021: 3210-3214 - [c14]George Dasoulas, Kevin Scaman, Aladin Virmaux:
Lipschitz normalization for self-attention layers with application to graph neural networks. ICML 2021: 2456-2466 - [c13]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: 30063-30074 - 2020
- [c12]George Dasoulas
, Ludovic Dos Santos, Kevin Scaman, Aladin Virmaux:
Coloring Graph Neural Networks for Node Disambiguation. IJCAI 2020: 2126-2132 - [c11]Kevin Scaman, Cédric Malherbe:
Robustness Analysis of Non-Convex Stochastic Gradient Descent using Biased Expectations. NeurIPS 2020 - [c10]Kevin Scaman, Ludovic Dos Santos, Merwan Barlier, Igor Colin:
A Simple and Efficient Smoothing Method for Faster Optimization and Local Exploration. NeurIPS 2020 - 2019
- [c9]Igor Colin, Ludovic Dos Santos, Kevin Scaman:
Theoretical Limits of Pipeline Parallel Optimization and Application to Distributed Deep Learning. NeurIPS 2019: 12350-12359 - 2018
- [c8]Kevin Scaman, Francis R. Bach, Sébastien Bubeck, Laurent Massoulié, Yin Tat Lee:
Optimal Algorithms for Non-Smooth Distributed Optimization in Networks. NeurIPS 2018: 2745-2754 - [c7]Aladin Virmaux, Kevin Scaman:
Lipschitz regularity of deep neural networks: analysis and efficient estimation. NeurIPS 2018: 3839-3848 - [c6]Moez Draief, Konstantin Kutzkov, Kevin Scaman, Milan Vojnovic:
KONG: Kernels for ordered-neighborhood graphs. NeurIPS 2018: 4055-4064 - 2017
- [c5]Rémi Lemonnier, Kevin Scaman, Argyris Kalogeratos:
Multivariate Hawkes Processes for Large-Scale Inference. AAAI 2017: 2168-2174 - [c4]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: 3027-3036 - 2015
- [c3]Kevin Scaman, Argyris Kalogeratos, Nicolas Vayatis:
A Greedy Approach for Dynamic Control of Diffusion Processes in Networks. ICTAI 2015: 652-659 - [c2]Kevin Scaman, Rémi Lemonnier, Nicolas Vayatis:
Anytime Influence Bounds and the Explosive Behavior of Continuous-Time Diffusion Networks. NIPS 2015: 2026-2034 - 2014
- [c1]Rémi Lemonnier, Kevin Scaman, Nicolas Vayatis:
Tight Bounds for Influence in Diffusion Networks and Application to Bond Percolation and Epidemiology. NIPS 2014: 846-854
Informal and Other Publications
- 2024
- [i16]Constantin Philippenko, Kevin Scaman, Laurent Massoulié:
In-depth Analysis of Low-rank Matrix Factorisation in a Federated Setting. CoRR abs/2409.08771 (2024) - 2023
- [i15]David A. R. Robin, Kevin Scaman, Marc Lelarge:
Convergence beyond the over-parameterized regime using Rayleigh quotients. CoRR abs/2301.08117 (2023) - [i14]Kevin Scaman:
Breaking the Log Barrier: a Novel Universal Restart Strategy for Faster Las Vegas Algorithms. CoRR abs/2304.11017 (2023) - [i13]Kevin Scaman, Mathieu Even, Laurent Massoulié:
Generalization Error of First-Order Methods for Statistical Learning with Generic Oracles. CoRR abs/2307.04679 (2023) - 2021
- [i12]George Dasoulas, Giannis Nikolentzos, Kevin Scaman, Aladin Virmaux, Michalis Vazirgiannis:
Ego-based Entropy Measures for Structural Representations on Graphs. CoRR abs/2102.08735 (2021) - [i11]Avery Ma, Aladin Virmaux, Kevin Scaman, Juwei Lu:
Improving Hierarchical Adversarial Robustness of Deep Neural Networks. CoRR abs/2102.09012 (2021) - [i10]George Dasoulas, Kevin Scaman, Aladin Virmaux:
Lipschitz Normalization for Self-Attention Layers with Application to Graph Neural Networks. CoRR abs/2103.04886 (2021) - [i9]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 abs/2106.01257 (2021) - 2020
- [i8]George Dasoulas, Giannis Nikolentzos, Kevin Scaman, Aladin Virmaux, Michalis Vazirgiannis:
Ego-based Entropy Measures for Structural Representations. CoRR abs/2003.00553 (2020) - 2019
- [i7]Igor Colin, Ludovic Dos Santos, Kevin Scaman:
Theoretical Limits of Pipeline Parallel Optimization and Application to Distributed Deep Learning. CoRR abs/1910.05104 (2019) - [i6]George Dasoulas, Ludovic Dos Santos, Kevin Scaman, Aladin Virmaux:
Coloring graph neural networks for node disambiguation. CoRR abs/1912.06058 (2019) - 2018
- [i5]Moez Draief, Konstantin Kutzkov, Kevin Scaman, Milan Vojnovic:
KONG: Kernels for ordered-neighborhood graphs. CoRR abs/1805.10014 (2018) - [i4]Kevin Scaman, Aladin Virmaux:
Lipschitz regularity of deep neural networks: analysis and efficient estimation. CoRR abs/1805.10965 (2018) - 2017
- [i3]Kevin Scaman, Argyris Kalogeratos, Luca Corinzia, Nicolas Vayatis:
A Spectral Method for Activity Shaping in Continuous-Time Information Cascades. CoRR abs/1709.05231 (2017) - 2014
- [i2]Rémi Lemonnier, Kevin Scaman, Nicolas Vayatis:
Tight Bounds for Influence in Diffusion Networks and Application to Bond Percolation and Epidemiology. CoRR abs/1407.4744 (2014) - [i1]Kevin Scaman, Argyris Kalogeratos, Nicolas Vayatis:
What Makes a Good Plan? An Efficient Planning Approach to Control Diffusion Processes in Networks. CoRR abs/1407.4760 (2014)
Coauthor Index
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