| Communication compression for decentralized training H Tang, S Gan, C Zhang, T Zhang, J Liu NeurIPS 2018, 2018 | 261 | 2018 |
| Towards Demystifying Serverless Machine Learning Training J Jiang*, S Gan*, Y Liu, F Wang, G Alonso, A Klimovic, A Singla, W Wu, ... SIGMOD 2021, 2021 | 85 | 2021 |
| 1-bit Adam: Communication Efficient Large-Scale Training with Adam's Convergence Speed H Tang, S Gan, AA Awan, S Rajbhandari, C Li, X Lian, J Liu, C Zhang, ... ICML 2021, 2021 | 55 | 2021 |
| Ease. ML: A Lifecycle Management System for Machine Learning L Aguilar Melgar, D Dao, S Gan, NM Gürel, N Hollenstein, J Jiang, ... CIDR 2021, 2021 | 24* | 2021 |
| Bagua: Scaling up Distributed Learning with System Relaxations S Gan, X Lian, R Wang, J Chang, C Liu, H Shi, S Zhang, X Li, T Sun, ... VLDB 2022, 2022 | 17 | 2022 |
| Fruda: Framework for distributed adversarial domain adaptation S Gan, A Mathur, A Isopoussu, F Kawsar, N Berthouze, ND Lane IEEE Transactions on Parallel and Distributed Systems 33 (11), 3153-3164, 2021 | 7 | 2021 |
| In-Database Machine Learning with CorgiPile: Stochastic Gradient Descent without Full Data Shuffle L Xu, S Qiu, B Yuan, J Jiang, C Renggli, S Gan, K Kara, G Li, J Liu, W Wu, ... SIGMOD 2022, 2022 | 1 | 2022 |
| Distributed Asynchronous Domain Adaptation: Towards Making Domain Adaptation More Practical in Real-World Systems S Gan, A Mathur, A Isopoussu, N Berthouze, ND Lane, F Kawsar Workshop on Systems for ML at NeurIPS 2019, 2019 | 1* | 2019 |
| A systematic evaluation of machine learning on serverless infrastructure J Jiang, S Gan, B Du, G Alonso, A Klimovic, A Singla, W Wu, S Wang, ... The VLDB Journal, 1-25, 2023 | | 2023 |
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