| Deterministic sequencing of exploration and exploitation for multi-armed bandit problems S Vakili, K Liu, Q Zhao IEEE Journal of Selected Topics in Signal Processing 7 (5), 759-767, 2013 | 119 | 2013 |
| Risk-averse multi-armed bandit problems under mean-variance measure S Vakili, Q Zhao IEEE Journal of Selected Topics in Signal Processing 10 (6), 1093-1111, 2016 | 96 | 2016 |
| On information gain and regret bounds in gaussian process bandits S Vakili, K Khezeli, V Picheny International Conference on Artificial Intelligence and Statistics, 82-90, 2021 | 80 | 2021 |
| Mean-variance and value at risk in multi-armed bandit problems S Vakili, Q Zhao 2015 53rd Annual Allerton Conference on Communication, Control, and …, 2015 | 39 | 2015 |
| Scalable Thompson Sampling using Sparse Gaussian Process Models S Vakili, H Moss, A Artemev, V Dutordoir, V Picheny Advances in Neural Information Processing Systems 34, 2021 | 31 | 2021 |
| Optimal order simple regret for Gaussian process bandits S Vakili, N Bouziani, S Jalali, A Bernacchia, D Shiu Advances in Neural Information Processing Systems 34, 21202-21215, 2021 | 26 | 2021 |
| A domain-shrinking based bayesian optimization algorithm with order-optimal regret performance S Salgia, S Vakili, Q Zhao Advances in Neural Information Processing Systems 34, 28836-28847, 2021 | 25 | 2021 |
| Adaptive sensor placement for continuous spaces JA Grant, A Boukouvalas, RR Griffiths, DS Leslie, S Vakili, EM De Cote http://proceedings.mlr.press/v97/grant19a.html 97, 2385-2393, 2019 | 18 | 2019 |
| Open problem: Tight online confidence intervals for rkhs elements S Vakili, J Scarlett, T Javidi Conference on Learning Theory, 4647-4652, 2021 | 16 | 2021 |
| Uniform generalization bounds for overparameterized neural networks S Vakili, M Bromberg, J Garcia, D Shiu, A Bernacchia arXiv preprint arXiv:2109.06099, 2021 | 14 | 2021 |
| Improved Convergence Rates for Sparse Approximation Methods in Kernel-Based Learning S Vakili, J Scarlett, D Shiu, A Bernacchia International Conference on Machine Learning (ICML), 2022 | 13 | 2022 |
| Regret Bounds for Noise-Free Kernel-Based Bandits S Vakili Conference on Learning Theory, 2022, 2020 | 12* | 2020 |
| A random walk approach to first-order stochastic convex optimization S Vakili, Q Zhao 2019 IEEE International Symposium on Information Theory (ISIT), 395-399, 2019 | 12 | 2019 |
| Hierarchical heavy hitter detection under unknown models S Vakili, Q Zhao, C Liu, CN Chuah 2018 IEEE International Conference on Acoustics, Speech and Signal …, 2018 | 12 | 2018 |
| Achieving complete learning in multi-armed bandit problems S Vakili, Q Zhao 2013 Asilomar Conference on Signals, Systems and Computers, 1778-1782, 2013 | 10 | 2013 |
| Risk-averse online learning under mean-variance measures S Vakili, Q Zhao 2015 IEEE International Conference on Acoustics, Speech and Signal …, 2015 | 9 | 2015 |
| Decision variance in risk-averse online learning S Vakili, A Boukouvalas, Q Zhao 2019 IEEE 58th Conference on Decision and Control (CDC), 2738-2744, 2019 | 8* | 2019 |
| Time-varying stochastic multi-armed bandit problems S Vakili, Q Zhao, Y Zhou 2014 48th Asilomar Conference on Signals, Systems and Computers, 2103-2107, 2014 | 8 | 2014 |
| Near-Optimal Collaborative Learning in Bandits C Réda, S Vakili, E Kaufmann Advances in Neural Information Processing Systems (NeurIPS), 2022 | 6 | 2022 |
| Stochastic Gradient Descent on a Tree: An Adaptive and Robust Approach to Stochastic Convex Optimization S Vakili, S Salgia, Q Zhao 2019 57th Annual Allerton Conference on Communication, Control, and …, 2019 | 6 | 2019 |
Qing ZhaoProfessor at Cornell UniversityVerified email at cornell.edu
