Kevin Scaman - RankMe

48 papers A* 19A 2B 1Misc 1Journal 22Unranked 3

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Unbiased Approximate Vector-Jacobian Products for Efficient Backpropagation. CoRR Killian Bakong, Laurent Massoulié, Edouard Oyallon, Kevin Scaman
2026	J	`jnl`	Variance-Reduced (ϵ,δ)-Unlearning using Forget Set Gradients. CoRR Martin Van Waerebeke, Marco Lorenzi, Kevin Scaman, El Mahdi El Mhamdi, Giovanni Neglia
2025	J	`jnl`	Adaptive collaboration for online personalized distributed learning with heterogeneous clients. CoRR Constantin Philippenko, Batiste Le Bars, Kevin Scaman, Laurent Massoulié
2025	A*	`conf`	In-depth Analysis of Low-rank Matrix Factorisation in a Federated Setting. AAAI Constantin Philippenko, Kevin Scaman, Laurent Massoulié
2025	A*	`conf`	When to Forget? Complexity Trade-offs in Machine Unlearning. ICML Martin Van Waerebeke, Marco Lorenzi, Giovanni Neglia, Kevin Scaman
2025	J	`jnl`	When to Forget? Complexity Trade-offs in Machine Unlearning. CoRR Martin Van Waerebeke, Marco Lorenzi, Giovanni Neglia, Kevin Scaman
2024	A*	`conf`	Improved Stability and Generalization Guarantees of the Decentralized SGD Algorithm. ICML Batiste Le Bars, Aurélien Bellet, Marc Tommasi, Kevin Scaman, Giovanni Neglia
2024	J	`jnl`	In-depth Analysis of Low-rank Matrix Factorisation in a Federated Setting. CoRR Constantin Philippenko, Kevin Scaman, Laurent Massoulié
2024	A	`conf`	Minimax Excess Risk of First-Order Methods for Statistical Learning with Data-Dependent Oracles. AISTATS Kevin Scaman, Mathieu Even, Batiste Le Bars, Laurent Massoulié
2024	A*	`conf`	Random Sparse Lifts: Construction, Analysis and Convergence of finite sparse networks. ICLR David A. R. Robin, Kevin Scaman, Marc Lelarge
2024	A	`conf`	SIFU: Sequential Informed Federated Unlearning for Efficient and Provable Client Unlearning in Federated Optimization. AISTATS Yann Fraboni, Martin Van Waerebeke, Kevin Scaman, Richard Vidal, Laetitia Kameni, Marco Lorenzi
2023	J	`jnl`	Breaking the Log Barrier: a Novel Universal Restart Strategy for Faster Las Vegas Algorithms. CoRR Kevin Scaman
2023	J	`jnl`	Convergence beyond the over-parameterized regime using Rayleigh quotients. CoRR David A. R. Robin, Kevin Scaman, Marc Lelarge
2023	J	`jnl`	Generalization Error of First-Order Methods for Statistical Learning with Generic Oracles. CoRR Kevin Scaman, Mathieu Even, Laurent Massoulié
2022	A*	`conf`	Convergence Rates of Non-Convex Stochastic Gradient Descent Under a Generic Lojasiewicz Condition and Local Smoothness. ICML Kevin Scaman, Cédric Malherbe, Ludovic Dos Santos
2022	A*	`conf`	Convergence beyond the over-parameterized regime using Rayleigh quotients. NeurIPS David A. R. Robin, Kevin Scaman, Marc Lelarge
2022	A*	`conf`	On Sample Optimality in Personalized Collaborative and Federated Learning. NeurIPS Mathieu Even, Laurent Massoulié, Kevin Scaman
2022	—	`conf`	Periodic signal recovery with regularized sine neural networks. NeurReps David A. R. Robin, Kevin Scaman, Marc Lelarge
2022	A*	`conf`	Robustness in Multi-Objective Submodular Optimization: a Quantile Approach. ICML Cédric Malherbe, Kevin Scaman
2021	Misc	`conf`	Ego-Based Entropy Measures for Structural Representations on Graphs. ICASSP George Dasoulas, Giannis Nikolentzos, Kevin Scaman, Aladin Virmaux, Michalis Vazirgiannis
2021	J	`jnl`	Ego-based Entropy Measures for Structural Representations on Graphs. CoRR George Dasoulas, Giannis Nikolentzos, Kevin Scaman, Aladin Virmaux, Michalis Vazirgiannis
2021	J	`jnl`	Improving Hierarchical Adversarial Robustness of Deep Neural Networks. CoRR Avery Ma, Aladin Virmaux, Kevin Scaman, Juwei Lu
2021	J	`jnl`	Lipschitz Normalization for Self-Attention Layers with Application to Graph Neural Networks. CoRR George Dasoulas, Kevin Scaman, Aladin Virmaux
2021	A*	`conf`	Lipschitz normalization for self-attention layers with application to graph neural networks. ICML George Dasoulas, Kevin Scaman, Aladin Virmaux
2021	A*	`conf`	Tight High Probability Bounds for Linear Stochastic Approximation with Fixed Stepsize. NeurIPS Alain Durmus, Eric Moulines, Alexey Naumov, Sergey Samsonov, Kevin Scaman, Hoi-To Wai
2021	J	`jnl`	Tight High Probability Bounds for Linear Stochastic Approximation with Fixed Stepsize. CoRR Alain Durmus, Eric Moulines, Alexey Naumov, Sergey Samsonov, Kevin Scaman, Hoi-To Wai
2020	A*	`conf`	A Simple and Efficient Smoothing Method for Faster Optimization and Local Exploration. NeurIPS Kevin Scaman, Ludovic Dos Santos, Merwan Barlier, Igor Colin
2020	A*	`conf`	Coloring Graph Neural Networks for Node Disambiguation. IJCAI George Dasoulas, Ludovic Dos Santos, Kevin Scaman, Aladin Virmaux
2020	J	`jnl`	Ego-based Entropy Measures for Structural Representations. CoRR George Dasoulas, Giannis Nikolentzos, Kevin Scaman, Aladin Virmaux, Michalis Vazirgiannis
2020	A*	`conf`	Robustness Analysis of Non-Convex Stochastic Gradient Descent using Biased Expectations. NeurIPS Kevin Scaman, Cédric Malherbe
2019	J	`jnl`	Coloring graph neural networks for node disambiguation. CoRR George Dasoulas, Ludovic Dos Santos, Kevin Scaman, Aladin Virmaux
2019	J	`jnl`	Optimal Convergence Rates for Convex Distributed Optimization in Networks. J. Mach. Learn. Res. Kevin Scaman, Francis R. Bach, Sébastien Bubeck, Yin Tat Lee, Laurent Massoulié
2019	A*	`conf`	Theoretical Limits of Pipeline Parallel Optimization and Application to Distributed Deep Learning. NeurIPS Igor Colin, Ludovic Dos Santos, Kevin Scaman
2019	J	`jnl`	Theoretical Limits of Pipeline Parallel Optimization and Application to Distributed Deep Learning. CoRR Igor Colin, Ludovic Dos Santos, Kevin Scaman
2018	A*	`conf`	KONG: Kernels for ordered-neighborhood graphs. NeurIPS Moez Draief, Konstantin Kutzkov, Kevin Scaman, Milan Vojnovic
2018	J	`jnl`	KONG: Kernels for ordered-neighborhood graphs. CoRR Moez Draief, Konstantin Kutzkov, Kevin Scaman, Milan Vojnovic
2018	A*	`conf`	Lipschitz regularity of deep neural networks: analysis and efficient estimation. NeurIPS Aladin Virmaux, Kevin Scaman
2018	J	`jnl`	Lipschitz regularity of deep neural networks: analysis and efficient estimation. CoRR Kevin Scaman, Aladin Virmaux
2018	A*	`conf`	Optimal Algorithms for Non-Smooth Distributed Optimization in Networks. NeurIPS Kevin Scaman, Francis R. Bach, Sébastien Bubeck, Laurent Massoulié, Yin Tat Lee
2017	J	`jnl`	A Spectral Method for Activity Shaping in Continuous-Time Information Cascades. CoRR Kevin Scaman, Argyris Kalogeratos, Luca Corinzia, Nicolas Vayatis
2017	A*	`conf`	Multivariate Hawkes Processes for Large-Scale Inference. AAAI Rémi Lemonnier, Kevin Scaman, Argyris Kalogeratos
2017	A*	`conf`	Optimal Algorithms for Smooth and Strongly Convex Distributed Optimization in Networks. ICML Kevin Scaman, Francis R. Bach, Sébastien Bubeck, Yin Tat Lee, Laurent Massoulié
2016	J	`jnl`	Suppressing Epidemics in Networks Using Priority Planning. IEEE Trans. Netw. Sci. Eng. Kevin Scaman, Argyris Kalogeratos, Nicolas Vayatis
2015	B	`conf`	A Greedy Approach for Dynamic Control of Diffusion Processes in Networks. ICTAI Kevin Scaman, Argyris Kalogeratos, Nicolas Vayatis
2015	—	`conf`	Anytime Influence Bounds and the Explosive Behavior of Continuous-Time Diffusion Networks. NIPS Kevin Scaman, Rémi Lemonnier, Nicolas Vayatis
2014	—	`conf`	Tight Bounds for Influence in Diffusion Networks and Application to Bond Percolation and Epidemiology. NIPS Rémi Lemonnier, Kevin Scaman, Nicolas Vayatis
2014	J	`jnl`	Tight Bounds for Influence in Diffusion Networks and Application to Bond Percolation and Epidemiology. CoRR Rémi Lemonnier, Kevin Scaman, Nicolas Vayatis
2014	J	`jnl`	What Makes a Good Plan? An Efficient Planning Approach to Control Diffusion Processes in Networks. CoRR Kevin Scaman, Argyris Kalogeratos, Nicolas Vayatis