← Authors

Kevin Scaman

23 papers · 2014–2025 · 7 conferences · across top CS/AI conferences

Achievements

πŸŒ‰ Interdisciplinary Bridge 🐝 Cross-Pollinator (10) 🌍 Conference Polyglot (7) πŸƒ Academic Marathon (11) 🐣 Hot Topic Early Bird πŸ—ΊοΈ Taxonomy Completionist (16) 🧭 Keyword Pioneer 🌈 Renaissance Researcher (5) 🧬 Topic Evolution πŸ† Keyword Champion (2) πŸ† Grand Slam πŸ—ƒοΈ Keyword Collector (103) πŸ’Ž Century Club (23) πŸ”₯ Unstoppable (6) πŸ“ˆ Trend Setter ❓ The Questioner

Conferences

NIPS (11) ICML (6) AISTATS (2) AAAI (1) ICLR (1) IJCAI (1) JMLR (1)

Top co-authors

Laurent MassouliΓ© (6) Ludovic DOS SANTOS (4) Aladin Virmaux (3) Francis Bach (3) Yin Tat Lee (3) Cedric Malherbe (3) SΓ©bastien Bubeck (3) Martin Van Waerebeke (2) Marco Lorenzi (2) David A. R. Robin (2)

Keywords

convergence rate (5) non-convex optimization (5) network optimization (3) federated learning (3) distributed optimization (3) neural network optimization (2) graph neural network (2) gradient descent (2) convex optimization (2) stochastic gradient descent (2) spectral radius (2) randomized smoothing (2) primal-dual algorithm (2) lipschitz continuity (2) diffusion network (2) privacy preservation (1) sample complexity (1) adversarial robustness (1) convergence analysis (1) graph classification (1)

Papers

In-depth Analysis of Low-rank Matrix Factorisation in a Federated Setting AAAI 2025
When to Forget? Complexity Trade-offs in Machine Unlearning ICML 2025
SIFU: Sequential Informed Federated Unlearning for Efficient and Provable Client Unlearning in Federated Optimization AISTATS 2024
Improved Stability and Generalization Guarantees of the Decentralized SGD Algorithm ICML 2024
Random Sparse Lifts: Construction, Analysis and Convergence of finite sparse networks ICLR 2024
Minimax Excess Risk of First-Order Methods for Statistical Learning with Data-Dependent Oracles AISTATS 2024
Robustness in Multi-Objective Submodular Optimization: a Quantile Approach ICML 2022
Convergence Rates of Non-Convex Stochastic Gradient Descent Under a Generic Lojasiewicz Condition and Local Smoothness ICML 2022
On Sample Optimality in Personalized Collaborative and Federated Learning NIPS 2022
Convergence beyond the over-parameterized regime using Rayleigh quotients NIPS 2022
Lipschitz normalization for self-attention layers with application to graph neural networks ICML 2021
Tight High Probability Bounds for Linear Stochastic Approximation with Fixed Stepsize NIPS 2021
Robustness Analysis of Non-Convex Stochastic Gradient Descent using Biased Expectations NIPS 2020
A Simple and Efficient Smoothing Method for Faster Optimization and Local Exploration NIPS 2020
Coloring Graph Neural Networks for Node Disambiguation IJCAI 2020
Optimal Convergence Rates for Convex Distributed Optimization in Networks JMLR 2019
Theoretical Limits of Pipeline Parallel Optimization and Application to Distributed Deep Learning NIPS 2019
Lipschitz regularity of deep neural networks: analysis and efficient estimation NIPS 2018
KONG: Kernels for ordered-neighborhood graphs NIPS 2018
Optimal Algorithms for Non-Smooth Distributed Optimization in Networks NIPS 2018
Optimal Algorithms for Smooth and Strongly Convex Distributed Optimization in Networks ICML 2017
Anytime Influence Bounds and the Explosive Behavior of Continuous-Time Diffusion Networks NIPS 2015
Tight Bounds for Influence in Diffusion Networks and Application to Bond Percolation and Epidemiology NIPS 2014