| Stochastic models of load balancing and scheduling in cloud computing clusters ST Maguluri, R Srikant, L Ying 2012 Proceedings IEEE Infocom, 702-710, 2012 | 440 | 2012 |
| Finite-time analysis of distributed TD (0) with linear function approximation on multi-agent reinforcement learning T Doan, S Maguluri, J Romberg International Conference on Machine Learning, 1626-1635, 2019 | 148 | 2019 |
| Scheduling jobs with unknown duration in clouds ST Maguluri, R Srikant IEEE/ACM Transactions On Networking 22 (6), 1938-1951, 2013 | 139 | 2013 |
| Performance of Q-learning with Linear Function Approximation: Stability and Finite-Time Analysis Z Chen, S Zhang, T Doan, ST Maguluri, JP Clarke arXiv preprint arXiv:1905.11425, 2019 | 127* | 2019 |
| Heavy traffic optimal resource allocation algorithms for cloud computing clusters ST Maguluri, R Srikant, L Ying Performance Evaluation 81, 20-39, 2014 | 114 | 2014 |
| Heavy traffic queue length behavior in a switch under the MaxWeight algorithm ST Maguluri, R Srikant Stochastic Systems 6 (1), 211-250, 2016 | 94* | 2016 |
| Finite-sample analysis of contractive stochastic approximation using smooth convex envelopes Z Chen, ST Maguluri, S Shakkottai, K Shanmugam Advances in Neural Information Processing Systems 33, 8223-8234, 2020 | 79* | 2020 |
| Fast convergence rates of distributed subgradient methods with adaptive quantization TT Doan, ST Maguluri, J Romberg IEEE Transactions on Automatic Control 66 (5), 2191-2205, 2020 | 70* | 2020 |
| Convergence rates of distributed gradient methods under random quantization: A stochastic approximation approach TT Doan, ST Maguluri, J Romberg IEEE Transactions on Automatic Control 66 (10), 4469-4484, 2020 | 62* | 2020 |
| On the linear convergence of natural policy gradient algorithm S Khodadadian, PR Jhunjhunwala, SM Varma, ST Maguluri 2021 60th IEEE Conference on Decision and Control (CDC), 3794-3799, 2021 | 56 | 2021 |
| A Lyapunov theory for finite-sample guarantees of asynchronous Q-learning and TD-learning variants Z Chen, ST Maguluri, S Shakkottai, K Shanmugam arXiv preprint arXiv:2102.01567, 2021 | 55 | 2021 |
| Federated reinforcement learning: Linear speedup under markovian sampling S Khodadadian, P Sharma, G Joshi, ST Maguluri International Conference on Machine Learning, 10997-11057, 2022 | 51 | 2022 |
| Finite-time performance of distributed temporal-difference learning with linear function approximation TT Doan, ST Maguluri, J Romberg SIAM Journal on Mathematics of Data Science 3 (1), 298-320, 2021 | 48 | 2021 |
| Heavy-traffic insensitive bounds for weighted proportionally fair bandwidth sharing policies W Wang, ST Maguluri, R Srikant, L Ying Mathematics of Operations Research 47 (4), 2691-2720, 2022 | 45* | 2022 |
| Finite-sample analysis of two-time-scale natural actor–critic algorithm S Khodadadian, TT Doan, J Romberg, ST Maguluri IEEE Transactions on Automatic Control 68 (6), 3273-3284, 2022 | 43 | 2022 |
| Optimal heavy-traffic queue length scaling in an incompletely saturated switch ST Maguluri, SK Burle, R Srikant Proceedings of the 2016 ACM SIGMETRICS International Conference on …, 2016 | 39 | 2016 |
| Finite-sample analysis of off-policy natural actor-critic algorithm S Khodadadian, Z Chen, ST Maguluri International Conference on Machine Learning, 5420-5431, 2021 | 36 | 2021 |
| Finite-sample analysis of off-policy natural actor–critic with linear function approximation Z Chen, S Khodadadian, ST Maguluri IEEE Control Systems Letters 6, 2611-2616, 2022 | 35 | 2022 |
| Transform methods for heavy-traffic analysis D Hurtado-Lange, ST Maguluri Stochastic Systems 10 (4), 275-309, 2020 | 34 | 2020 |
| Dynamic pricing and matching for two-sided queues SM Varma, P Bumpensanti, ST Maguluri, H Wang Operations Research 71 (1), 83-100, 2023 | 22 | 2023 |
R. SrikantUniversity of Illinois at Urbana-Champaign在 illinois.edu 的电子邮件经过验证
