Követés
Nima Hamidi
Nima Hamidi
Quantitative Researcher
E-mail megerősítve itt: stanford.edu - Kezdőlap
Cím
Hivatkozott rá
Hivatkozott rá
Év
The Unreasonable Effectiveness of Greedy Algorithms in Multi-Armed Bandit with Many Arms
M Bayati, N Hamidi, R Johari, K Khosravi
Advances in Neural Information Processing Systems 33, 2020
422020
On Worst-case Regret of Linear Thompson Sampling
N Hamidi, M Bayati
arXiv preprint arXiv:2006.06790, 2020
312020
On low-rank trace regression under general sampling distribution
N Hamidi, M Bayati
The Journal of Machine Learning Research 23 (1), 14424-14472, 2022
242022
A General Theory of the Stochastic Linear Bandit and Its Applications
N Hamidi, M Bayati
arXiv preprint arXiv:2002.05152, 2020
8*2020
Personalizing many decisions with high-dimensional covariates
N Hamidi, M Bayati, K Gupta
Advances in Neural Information Processing Systems 32, 2019
82019
The Elliptical Potential Lemma for General Distributions with an Application to Linear Thompson Sampling
N Hamidi, M Bayati
Operations Research, 2022
4*2022
Minimax Regret Bounds for Stochastic Linear Bandit Algorithms
N Hamidi
Stanford University, 2021
2021
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Cikkek 1–7