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Nima Hamidi
Nima Hamidi
Quantitative Researcher
Verified email at stanford.edu - Homepage
Title
Cited by
Cited by
Year
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
292020
On Worst-case Regret of Linear Thompson Sampling
N Hamidi, M Bayati
arXiv preprint arXiv:2006.06790, 2020
262020
On low-rank trace regression under general sampling distribution
N Hamidi, M Bayati
The Journal of Machine Learning Research 23 (1), 14424-14472, 2022
172022
A General Theory of the Stochastic Linear Bandit and Its Applications
N Hamidi, M Bayati
arXiv preprint arXiv:2002.05152, 2020
6*2020
Personalizing many decisions with high-dimensional covariates
N Hamidi, M Bayati, K Gupta
Advances in Neural Information Processing Systems 32, 2019
52019
The Elliptical Potential Lemma for General Distributions with an Application to Linear Thompson Sampling
N Hamidi, M Bayati
Operations Research, 2022
3*2022
Minimax Regret Bounds for Stochastic Linear Bandit Algorithms
N Hamidi
Stanford University, 2021
2021
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Articles 1–7