| 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 | 40 | 2020 |
| On Worst-case Regret of Linear Thompson Sampling N Hamidi, M Bayati arXiv preprint arXiv:2006.06790, 2020 | 26 | 2020 |
| On low-rank trace regression under general sampling distribution N Hamidi, M Bayati The Journal of Machine Learning Research 23 (1), 14424-14472, 2022 | 23 | 2022 |
| 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 | 7 | 2019 |
| 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 |
Mohsen BayatiProfessor, Stanford UniversityE-mail confirmado em stanford.edu