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Kevin Scaman
Kevin Scaman
Research scientist, INRIA
Verified email at inria.fr - Homepage
Title
Cited by
Cited by
Year
Lipschitz regularity of deep neural networks: analysis and efficient estimation
A Virmaux, K Scaman
Advances in Neural Information Processing Systems 31, 2018
2502018
Optimal algorithms for smooth and strongly convex distributed optimization in networks
K Scaman, F Bach, S Bubeck, YT Lee, L Massoulié
Proceedings of the 34th International Conference on Machine Learning-Volume …, 2017
2482017
Optimal algorithms for non-smooth distributed optimization in networks
K Scaman, F Bach, S Bubeck, L Massoulié, YT Lee
Advances in Neural Information Processing Systems 31, 2018
1392018
Optimal convergence rates for convex distributed optimization in networks
K Scaman, F Bach, S Bubeck, Y Lee, L Massoulié
Journal of Machine Learning Research 20, 1-31, 2019
472019
Multivariate Hawkes processes for large-scale inference
R Lemonnier, K Scaman, A Kalogeratos
Proceedings of the AAAI conference on artificial intelligence 31 (1), 2017
322017
Coloring graph neural networks for node disambiguation
G Dasoulas, LD Santos, K Scaman, A Virmaux
arXiv preprint arXiv:1912.06058, 2019
302019
Suppressing epidemics in networks using priority planning
K Scaman, A Kalogeratos, N Vayatis
IEEE Transactions on Network Science and Engineering 3 (4), 271-285, 2016
29*2016
Tight bounds for influence in diffusion networks and application to bond percolation and epidemiology
R Lemonnier, K Scaman, N Vayatis
Advances in Neural Information Processing Systems 27, 2014
212014
A greedy approach for dynamic control of diffusion processes in networks
K Scaman, A Kalogeratos, N Vayatis
2015 IEEE 27th International Conference on Tools with Artificial …, 2015
142015
Robustness analysis of non-convex stochastic gradient descent using biased expectations
K Scaman, C Malherbe
Advances in Neural Information Processing Systems 33, 16377-16387, 2020
132020
Anytime influence bounds and the explosive behavior of continuous-time diffusion networks
K Scaman, R Lemonnier, N Vayatis
Advances in Neural Information Processing Systems 28, 2015
122015
Theoretical limits of pipeline parallel optimization and application to distributed deep learning
I Colin, L Dos Santos, K Scaman
Advances in Neural Information Processing Systems 32, 2019
62019
Dynamic treatment allocation for epidemic control in arbitrary networks
K Scaman, A Kalogeratos, N Vayatis
Diffusion Networks and Cascade Analytics workshop, 2014
62014
Ego-based entropy measures for structural representations on graphs
G Dasoulas, G Nikolentzos, K Scaman, A Virmaux, M Vazirgiannis
ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and …, 2021
5*2021
Spectral bounds in random graphs applied to spreading phenomena and percolation
R Lemonnier, K Scaman, N Vayatis
Advances in Applied Probability 50 (2), 480-503, 2018
42018
Information diffusion and rumor spreading
A Kalogeratos, K Scaman, L Corinzia, N Vayatis
Cooperative and Graph Signal Processing, 651-678, 2018
42018
A spectral method for activity shaping in continuous-time information cascades
K Scaman, A Kalogeratos, L Corinzia, N Vayatis
arXiv preprint arXiv:1709.05231, 2017
42017
Learning to suppress SIS epidemics in networks
A Kalogeratos, K Scaman, N Vayatis
Networks in the Social and Information Sciences workshop, 2015
42015
Lipschitz normalization for self-attention layers with application to graph neural networks
G Dasoulas, K Scaman, A Virmaux
International Conference on Machine Learning, 2456-2466, 2021
32021
KONG: Kernels for ordered-neighborhood graphs
M Draief, K Kutzkov, K Scaman, M Vojnovic
Advances in Neural Information Processing Systems 31, 2018
32018
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