| Resource Management with Deep Reinforcement Learning H Mao, M Alizadeh, I Menache, S Kandula HotNets, 50-56, 2016 | 1072 | 2016 |
| Neural adaptive video streaming with pensieve H Mao, R Netravali, M Alizadeh Proceedings of the Conference of the ACM Special Interest Group on Data …, 2017 | 987 | 2017 |
| Smart homes that monitor breathing and heart rate F Adib, H Mao, Z Kabelac, D Katabi, RC Miller Proceedings of the 33rd annual ACM conference on human factors in computing …, 2015 | 806 | 2015 |
| Learning scheduling algorithms for data processing clusters H Mao, M Schwarzkopf, SB Venkatakrishnan, Z Meng, M Alizadeh Proceedings of the Conference of the ACM Special Interest Group on Data …, 2018 | 511 | 2018 |
| Capturing the human figure through a wall F Adib, CY Hsu, H Mao, D Katabi, F Durand ACM Transactions on Graphics (TOG) 34 (6), 1-13, 2015 | 389 | 2015 |
| Neo: A learned query optimizer R Marcus, P Negi, H Mao, C Zhang, M Alizadeh, T Kraska, ... Proceedings of the 45th international conference on very large data bases, 2019 | 271 | 2019 |
| Sagedb: A learned database system T Kraska, M Alizadeh, A Beutel, EH Chi, J Ding, A Kristo, G Leclerc, ... 9th Biennial Conference on Innovative Data Systems Research, 2019 | 169 | 2019 |
| Numfabric: Fast and flexible bandwidth allocation in datacenters K Nagaraj, D Bharadia, H Mao, S Chinchali, M Alizadeh, S Katti Proceedings of the 2016 ACM SIGCOMM Conference, 188-201, 2016 | 111 | 2016 |
| Bao: Making learned query optimization practical R Marcus, P Negi, H Mao, N Tatbul, M Alizadeh, T Kraska ACM SIGMOD Record 51 (1), 6-13, 2022 | 109* | 2022 |
| Placeto: Learning Generalizable Device Placement Algorithms for Distributed Machine Learning R Addanki, SB Venkatakrishnan, S Gupta, H Mao, M Alizadeh Neural Information Processing Systems (NeurIPS), 2019 | 96* | 2019 |
| Interpreting Deep Learning-Based Networking Systems Z Meng, M Wang, M Xu, H Mao, J Bai, H Hu Proceedings of the Conference of the ACM Special Interest Group on Data …, 2019 | 82 | 2019 |
| Variance Reduction for Reinforcement Learning in Input-Driven Environments H Mao, SB Venkatakrishnan, M Schwarzkopf, M Alizadeh International Conference on Learning Representations (ICLR), 2018 | 77 | 2018 |
| Park: An Open Platform for Learning Augmented Computer Systems H Mao, P Negi, A Narayan, H Wang, J Yang, H Wang, R Marcus, ... Neural Information Processing Systems (NeurIPS), 2019 | 69 | 2019 |
| Real-world Video Adaptation with Reinforcement Learning H Mao, S Chen, D Dimmery, S Singh, D Blaisdell, Y Tian, M Alizadeh, ... ICML Reinforcement Learning for Real Life Workshop, 2019 | 46 | 2019 |
| Real-time breath monitoring using wireless signals F Adib, Z Kabelac, H Mao, D Katabi, RC Miller Proceedings of the 20th annual international conference on Mobile computing …, 2014 | 33 | 2014 |
| Cost-guided cardinality estimation: Focus where it matters P Negi, R Marcus, H Mao, N Tatbul, T Kraska, M Alizadeh 2020 IEEE 36th International Conference on Data Engineering Workshops (ICDEW …, 2020 | 28 | 2020 |
| Flow-loss: Learning cardinality estimates that matter P Negi, R Marcus, A Kipf, H Mao, N Tatbul, T Kraska, M Alizadeh arXiv preprint arXiv:2101.04964, 2021 | 26 | 2021 |
| Towards Safe Online Reinforcement Learning in Computer Systems H Mao, M Schwarzkopf, H He, M Alizadeh NeurIPS Machine Learning for Systems Workshop, 2019 | 24 | 2019 |
| Learned garbage collection L Cen, R Marcus, H Mao, J Gottschlich, M Alizadeh, T Kraska Proceedings of the 4th ACM SIGPLAN International Workshop on Machine …, 2020 | 12 | 2020 |
| Learning Caching Policies with Subsampling H Wang, H He, M Alizadeh, H Mao NeurIPS Machine Learning for Systems Workshop, 2019 | 11 | 2019 |
Mohammad AlizadehAssociate Professor of Computer Science, MITcsail.mit.edu의 이메일 확인됨
