| Solving bernoulli rank-one bandits with unimodal thompson sampling C Trinh, E Kaufmann, C Vernade, R Combes Algorithmic Learning Theory, 862-889, 2020 | 26 | 2020 |
| Mlperf mobile inference benchmark: An industry-standard open-source machine learning benchmark for on-device ai V Janapa Reddi, D Kanter, P Mattson, J Duke, T Nguyen, R Chukka, ... Proceedings of Machine Learning and Systems 4, 352-369, 2022 | 20 | 2022 |
| Towards optimal algorithms for multi-player bandits without collision sensing information W Huang, R Combes, C Trinh Conference on Learning Theory, 1990-2012, 2022 | 12 | 2022 |
| MLPerf mobile inference benchmark VJ Reddi, D Kanter, P Mattson, J Duke, T Nguyen, R Chukka, K Shiring, ... arXiv preprint arXiv:2012.02328, 2020 | 9 | 2020 |
| MLPerf mobile inference benchmark: Why mobile AI benchmarking is hard and what to do about it VJ Reddi, D Kanter, P Mattson, J Duke, T Nguyen, R Chukka, K Shiring, ... arXiv preprint arXiv:2012.02328, 2020 | 4 | 2020 |
| Solving Bernoulli rank-one bandits with unimodal Thompson sampling C Trinh, E Kaufmann, C Vernade, R Combes arXiv preprint arXiv:1912.03074, 2019 | 3 | 2019 |
| A High Performance, Low Complexity Algorithm for Multi-Player Bandits Without Collision Sensing Information C Trinh, R Combes arXiv preprint arXiv:2102.10200, 2021 | | 2021 |
Richard CombesAssistant Professor, SupélecVerified email at supelec.fr
