| Lipschitz regularity of deep neural networks: analysis and efficient estimation A Virmaux, K Scaman Advances in Neural Information Processing Systems 31, 2018 | 602 | 2018 |
| 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 | 369 | 2017 |
| 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 | 190 | 2018 |
| Optimal convergence rates for convex distributed optimization in networks K Scaman, F Bach, S Bubeck, YT Lee, L Massoulié Journal of Machine Learning Research 20 (159), 1-31, 2019 | 89 | 2019 |
| Coloring graph neural networks for node disambiguation G Dasoulas, LD Santos, K Scaman, A Virmaux arXiv preprint arXiv:1912.06058, 2019 | 88 | 2019 |
| Multivariate Hawkes processes for large-scale inference R Lemonnier, K Scaman, A Kalogeratos Proceedings of the AAAI conference on artificial intelligence 31 (1), 2017 | 48 | 2017 |
| 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 | 39 | 2021 |
| 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 | 36* | 2016 |
| 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 | 29 | 2020 |
| 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 | 26 | 2014 |
| Tight high probability bounds for linear stochastic approximation with fixed stepsize A Durmus, E Moulines, A Naumov, S Samsonov, K Scaman, HT Wai Advances in Neural Information Processing Systems 34, 30063-30074, 2021 | 25 | 2021 |
| Sequential informed federated unlearning: Efficient and provable client unlearning in federated optimization Y Fraboni, M Van Waerebeke, K Scaman, R Vidal, L Kameni, M Lorenzi arXiv preprint arXiv:2211.11656, 2022 | 21 | 2022 |
| 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 | 19 | 2015 |
| Convergence rates of non-convex stochastic gradient descent under a generic lojasiewicz condition and local smoothness K Scaman, C Malherbe, L Dos Santos International conference on machine learning, 19310-19327, 2022 | 18 | 2022 |
| On sample optimality in personalized collaborative and federated learning M Even, L Massoulié, K Scaman Advances in Neural Information Processing Systems 35, 212-225, 2022 | 16* | 2022 |
| 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 | 14 | 2015 |
| 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 | 8* | 2021 |
| 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 | 8 | 2019 |
| Information diffusion and rumor spreading A Kalogeratos, K Scaman, L Corinzia, N Vayatis Cooperative and Graph Signal Processing, 651-678, 2018 | 7 | 2018 |
| Improving hierarchical adversarial robustness of deep neural networks A Ma, A Virmaux, K Scaman, J Lu arXiv preprint arXiv:2102.09012, 2021 | 6 | 2021 |
Laurent MassoulieInria, MSR-Inria Joint CentreVerified email at inria.fr
