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Cindy Trinh Sridykhan
Cindy Trinh Sridykhan
ENS Paris-Saclay
Verified email at ens-paris-saclay.fr - Homepage
Title
Cited by
Cited by
Year
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
342022
Solving bernoulli rank-one bandits with unimodal thompson sampling
C Trinh, E Kaufmann, C Vernade, R Combes
Algorithmic Learning Theory, 862-889, 2020
302020
Towards optimal algorithms for multi-player bandits without collision sensing information
W Huang, R Combes, C Trinh
Conference on Learning Theory, 1990-2012, 2022
142022
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
42020
Solving Bernoulli rank-one bandits with unimodal Thompson sampling
C Trinh, E Kaufmann, C Vernade, R Combes
arXiv preprint arXiv:1912.03074, 2019
32019
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
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Articles 1–6