| Coloring graph neural networks for node disambiguation G Dasoulas, LD Santos, K Scaman, A Virmaux arXiv preprint arXiv:1912.06058, 2019 | 81 | 2019 |
| Multilabel classification on heterogeneous graphs with gaussian embeddings L Dos Santos, B Piwowarski, P Gallinari Machine Learning and Knowledge Discovery in Databases: European Conference …, 2016 | 42 | 2016 |
| Gaussian embeddings for collaborative filtering L Dos Santos, B Piwowarski, P Gallinari Proceedings of the 40th international ACM SIGIR conference on research and …, 2017 | 19 | 2017 |
| Representation learning for classification in heterogeneous graphs with application to social networks LD Santos, B Piwowarski, L Denoyer, P Gallinari ACM Transactions on Knowledge Discovery from Data (TKDD) 12 (5), 1-33, 2018 | 15 | 2018 |
| 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 | 13 | 2022 |
| 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 |
| Unifying gans and score-based diffusion as generative particle models JY Franceschi, M Gartrell, L Dos Santos, T Issenhuth, E de Bézenac, ... Advances in Neural Information Processing Systems 36, 2024 | 7 | 2024 |
| A simple and efficient smoothing method for faster optimization and local exploration K Scaman, L Dos Santos, M Barlier, I Colin Advances in Neural Information Processing Systems 33, 6503-6513, 2020 | 4 | 2020 |
| Density estimation for conservative q-Learning P Daoudi, L Dos Santos, M Barlier, A Virmaux ICLR 2022 Workshop on Generalizable Policy Learning in Physical World, 2022 | 3 | 2022 |
| OSNR prediction for optical links via learned noise figures S Kamel, H Hafermann, D Le Gac, L Dos Santos, B Kégl, Y Frignac, ... 2021 European Conference on Optical Communication (ECOC), 1-4, 2021 | 3 | 2021 |
| Enhancing reinforcement learning agents with local guides P Daoudi, B Robu, C Prieur, LD Santos, M Barlier arXiv preprint arXiv:2402.13930, 2024 | 1 | 2024 |
| Meta-learning of black-box solvers using deep reinforcement learning S Chaybouti, L Dos Santos, C Malherbe, A Virmaux NeurIPS 2022, MetaLearn Workshop, 2022 | 1 | 2022 |
| Node disambiguation G Dasoulas, L Dos Santos, K Scaman, A Virmaux US Patent App. 17/702,064, 2022 | 1 | 2022 |
| Representation learning for relational data L Dos Santos Université Pierre et Marie Curie-Paris VI, 2017 | 1 | 2017 |
| Modelling Relational Time Series using Gaussian Embeddings L Dos Santos, A Ziat, L Denoyer, B Piwowarski, P Gallinari | 1 | 2016 |
| Improving a Proportional Integral Controller with Reinforcement Learning on a Throttle Valve Benchmark P Daoudi, B Mavkov, B Robu, C Prieur, E Witrant, M Barlier, LD Santos arXiv preprint arXiv:2402.13654, 2024 | | 2024 |
| A Trust Region Approach for Few-Shot Sim-to-Real Reinforcement Learning P Daoudi, C Prieur, B Robu, M Barlier, LD Santos arXiv preprint arXiv:2312.15474, 2023 | | 2023 |
| Method and system for a controller L Dos Santos, M Barlier, K Balazs, I Colin US Patent App. 18/065,800, 2023 | | 2023 |
| NODE DISAMBIGUATION G Dasoulas, L dos Santos, K Scaman, A Virmaux | | 2022 |
| HAL Id: hal-01582488 L Dos Santos, B Piwowarski, P Gallinari | | 2017 |
