Tight Bounds for Influence in Diffusion Networks and Application to Bond Percolation and Epidemiology. - Rémi Lemonnier; Kevin Scaman; Nicolas Vayatis (NIPS, 2014)

What Makes a Good Plan? An Efficient Planning Approach to Control Diffusion Processes in Networks. - Kevin Scaman; Argyris Kalogeratos; Nicolas Vayatis (CoRR, 2014)

A Greedy Approach for Dynamic Control of Diffusion Processes in Networks. - Kevin Scaman; Argyris Kalogeratos; Nicolas Vayatis (ICTAI, 2015)

Anytime Influence Bounds and the Explosive Behavior of Continuous-Time Diffusion Networks. - Kevin Scaman; Rémi Lemonnier; Nicolas Vayatis (NIPS, 2015)

Suppressing Epidemics in Networks Using Priority Planning. - Kevin Scaman; Argyris Kalogeratos; Nicolas Vayatis (IEEE Trans. Netw. Sci. Eng., 2016)

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

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

A Spectral Method for Activity Shaping in Continuous-Time Information Cascades. - Kevin Scaman; Argyris Kalogeratos; Luca Corinzia; Nicolas Vayatis (CoRR, 2017)

KONG: Kernels for ordered-neighborhood graphs. - Moez Draief; Konstantin Kutzkov; Kevin Scaman; Milan Vojnovic (NeurIPS, 2018)

Optimal Algorithms for Non-Smooth Distributed Optimization in Networks. - Kevin Scaman; Francis R. Bach; Sébastien Bubeck; Laurent Massoulié; Yin Tat Lee (NeurIPS, 2018)

Lipschitz regularity of deep neural networks: analysis and efficient estimation. - Aladin Virmaux; Kevin Scaman (NeurIPS, 2018)

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

Optimal Convergence Rates for Convex Distributed Optimization in Networks. - Kevin Scaman; Francis R. Bach; Sébastien Bubeck; Yin Tat Lee; Laurent Massoulié (J. Mach. Learn. Res., 2019)

Coloring Graph Neural Networks for Node Disambiguation. - George Dasoulas; Ludovic Dos Santos; Kevin Scaman; Aladin Virmaux (IJCAI, 2020)

Robustness Analysis of Non-Convex Stochastic Gradient Descent using Biased Expectations. - Kevin Scaman; Cédric Malherbe (NeurIPS, 2020)

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

Ego-based Entropy Measures for Structural Representations. - George Dasoulas; Giannis Nikolentzos; Kevin Scaman; Aladin Virmaux; Michalis Vazirgiannis (CoRR, 2020)

Ego-Based Entropy Measures for Structural Representations on Graphs. - George Dasoulas; Giannis Nikolentzos; Kevin Scaman; Aladin Virmaux; Michalis Vazirgiannis (ICASSP, 2021)

Lipschitz normalization for self-attention layers with application to graph neural networks. - George Dasoulas; Kevin Scaman; Aladin Virmaux (ICML, 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, 2021)

Improving Hierarchical Adversarial Robustness of Deep Neural Networks. - Avery Ma; Aladin Virmaux; Kevin Scaman; Juwei Lu (CoRR, 2021)

Robustness in Multi-Objective Submodular Optimization: a Quantile Approach. - Cédric Malherbe; Kevin Scaman (ICML, 2022)

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

Periodic signal recovery with regularized sine neural networks. - David A. R. Robin; Kevin Scaman; Marc Lelarge (NeurReps, 2022)

On Sample Optimality in Personalized Collaborative and Federated Learning. - Mathieu Even; Laurent Massoulié; Kevin Scaman (NeurIPS, 2022)

Convergence beyond the over-parameterized regime using Rayleigh quotients. - David A. R. Robin; Kevin Scaman; Marc Lelarge (NeurIPS, 2022)

Breaking the Log Barrier: a Novel Universal Restart Strategy for Faster Las Vegas Algorithms. - Kevin Scaman (CoRR, 2023)

Generalization Error of First-Order Methods for Statistical Learning with Generic Oracles. - Kevin Scaman; Mathieu Even; Laurent Massoulié (CoRR, 2023)

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 (AISTATS, 2024)

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

Random Sparse Lifts: Construction, Analysis and Convergence of finite sparse networks. - David A. R. Robin; Kevin Scaman; Marc Lelarge (ICLR, 2024)

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

In-depth Analysis of Low-rank Matrix Factorisation in a Federated Setting. - Constantin Philippenko; Kevin Scaman; Laurent Massoulié (AAAI, 2025)

When to Forget? Complexity Trade-offs in Machine Unlearning. - Martin Van Waerebeke; Marco Lorenzi; Giovanni Neglia; Kevin Scaman (CoRR, 2025)

Adaptive collaboration for online personalized distributed learning with heterogeneous clients. - Constantin Philippenko; Batiste Le Bars; Kevin Scaman; Laurent Massoulié (CoRR, 2025)