| Attack of the tails: Yes, you really can backdoor federated learning H Wang, K Sreenivasan, S Rajput, H Vishwakarma, S Agarwal, J Sohn, ... Advances in Neural Information Processing Systems 33, 16070-16084, 2020 | 498 | 2020 |
| AutoFreeze: Automatically Freezing Model Blocks to Accelerate Fine-tuning Y Liu, S Agarwal, S Venkataraman arXiv preprint arXiv:2102.01386, 2021 | 43 | 2021 |
| Pufferfish: Communication-efficient models at no extra cost H Wang, S Agarwal, D Papailiopoulos Proceedings of Machine Learning and Systems 3, 365-386, 2021 | 38 | 2021 |
| On the utility of gradient compression in distributed training systems S Agarwal, H Wang, S Venkataraman, D Papailiopoulos Proceedings of Machine Learning and Systems 4, 652-672, 2022 | 37 | 2022 |
| Accordion: Adaptive gradient communication via critical learning regime identification S Agarwal, H Wang, K Lee, S Venkataraman, D Papailiopoulos arXiv preprint arXiv:2010.16248, 2020 | 18 | 2020 |
| Adaptive Gradient Communication via Critical Learning Regime Identification S Agarwal, H Wang, K Lee, S Venkataraman, D Papailiopoulos Proceedings of Machine Learning and Systems 3, 55-80, 2021 | 17 | 2021 |
| Bagpipe: Accelerating deep recommendation model training S Agarwal, C Yan, Z Zhang, S Venkataraman Proceedings of the 29th Symposium on Operating Systems Principles, 348-363, 2023 | 6 | 2023 |
| Cuttlefish: Low-rank Model Training without All The Tuning H Wang, S Agarwal, Y Tanaka, E Xing, D Papailiopoulos Proceedings of Machine Learning and Systems 5, 2023 | 3 | 2023 |
| CHAI: Clustered Head Attention for Efficient LLM Inference S Agarwal, B Acun, B Homer, M Elhoushi, Y Lee, S Venkataraman, ... arXiv preprint arXiv:2403.08058, 2024 | | 2024 |
| Decoding Speculative Decoding M Yan, S Agarwal, S Venkataraman arXiv preprint arXiv:2402.01528, 2024 | | 2024 |
| Blox: A Modular Toolkit for Deep Learning Schedulers S Agarwal, A Phanishayee, S Venkataraman arXiv preprint arXiv:2312.12621, 2023 | | 2023 |
| Method and system for efficient clustering of combined numeric and qualitative data records S Agarwal, B Aravindakshan, B Sudarshan, H Chandrasekaran US Patent 10,846,311, 2020 | | 2020 |
| Scalable K-Medoids via True Error Bound and Familywise Bandits A Babu, S Agarwal, S Babu, H Chandrasekaran arXiv preprint arXiv:1905.10979, 2019 | | 2019 |
Dimitris PapailiopoulosAssociate Professor, University of Wisconsin-Madisonpapail.io의 이메일 확인됨
