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Cindy Trinh Sridykhan
Cindy Trinh Sridykhan
ENS Paris-Saclay
Verifierad e-postadress på ens-paris-saclay.fr
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Solving bernoulli rank-one bandits with unimodal thompson sampling
C Trinh, E Kaufmann, C Vernade, R Combes
Algorithmic Learning Theory, 862-889, 2020
82020
Towards Optimal Algorithms for Multi-Player Bandits without Collision Sensing Information
W Huang, R Combes, C Trinh
arXiv preprint arXiv:2103.13059, 2021
42021
MLPerf mobile inference benchmark
VJ Reddi, D Kanter, P Mattson, J Duke, T Nguyen, R Chukka, K Shiring, ...
arXiv preprint arXiv:2012.02328, 2020
42020
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, ...
42020
Solving Bernoulli rank-one bandits with unimodal Thompson sampling
C Trinh, E Kaufmann, C Vernade, R Combes
arXiv preprint arXiv:1912.03074, 2019
42019
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, 2022
2022
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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Artiklar 1–7