| A Field Guide to Federated Optimization J Wang, Z Charles, Z Xu, G Joshi, HB McMahan, B Aguera y Arcas, ... arXiv preprint arXiv:2107.06917, 2021 | 317 | 2021 |
| The Sparse Recovery Autoencoder S Wu, AG Dimakis, S Sanghavi, FX Yu, D Holtmann-Rice, D Storcheus, ... arXiv preprint arXiv:1806.10175v1, 2018 | 180* | 2018 |
| Federated Reconstruction: Partially Local Federated Learning K Singhal, H Sidahmed, Z Garrett, S Wu, K Rush, S Prakash Advances in Neural Information Processing Systems (NeurIPS), 2021, 2021 | 123 | 2021 |
| Learning a Compressed Sensing Measurement Matrix via Gradient Unrolling S Wu, AG Dimakis, S Sanghavi, FX Yu, D Holtmann-Rice, D Storcheus, ... International Conference on Machine Learning (ICML), 2019 | 64 | 2019 |
| Sparse logistic regression learns all discrete pairwise graphical models S Wu, S Sanghavi, AG Dimakis Advances in Neural Information Processing Systems (NeurIPS 2019), 2019 | 60 | 2019 |
| Performance Study on a CSMA/CA-Based MAC Protocol for Multi-User MIMO Wireless LANs S Wu, W Mao, X Wang IEEE Transactions on Wireless Communications 13 (6), 3153-3166, 2014 | 46 | 2014 |
| Motley: Benchmarking heterogeneity and personalization in federated learning S Wu, T Li, Z Charles, Y Xiao, Z Liu, Z Xu, V Smith arXiv preprint arXiv:2206.09262, 2022 | 35 | 2022 |
| Learning Distributions Generated by One-Layer ReLU Networks S Wu, AG Dimakis, S Sanghavi Advances in Neural Information Processing Systems (NeurIPS 2019), 2019 | 27 | 2019 |
| Leveraging Sparsity for Efficient Submodular Data Summarization E Lindgren, S Wu, A Dimakis Advances in Neural Information Processing Systems (NIPS 2016), 2016 | 26 | 2016 |
| Implicit regularization and convergence for weight normalization X Wu, E Dobriban, T Ren, S Wu, Z Li, S Gunasekar, R Ward, Q Liu Advances in Neural Information Processing Systems 33, 2835-2847, 2020 | 16 | 2020 |
| Sparse and Greedy: Sparsifying Submodular Facility Location Problems E Lindgren, S Wu, A Dimakis NIPS workshop OPT 2015, 2015 | 15 | 2015 |
| Single Pass PCA of Matrix Products S Wu, S Bhojanapalli, S Sanghavi, A Dimakis Advances in Neural Information Processing Systems (NIPS 2016), 2016 | 9 | 2016 |
| Implicit Regularization of Normalization Methods X Wu, E Dobriban, T Ren, S Wu, Z Li, S Gunasekar, R Ward, Q Liu arXiv preprint arXiv:1911.07956, 2019 | 8 | 2019 |
| QoS-oriented distributed opportunistic scheduling for wireless networks with hybrid links W Mao, S Wu, X Wang IEEE Global Communications Conference (IEEE GLOBECOM 2013), 2013 | 5 | 2013 |
| Performance analysis of random access multi-user MIMO wireless LANs S Wu, W Mao, X Wang IEEE Global Communications Conference (IEEE GLOBECOM 2013), 2013 | 5 | 2013 |
| Distributed Opportunistic Scheduling with QoS Constraints for Wireless Networks with Hybrid Links W Mao, X Wang, S Wu IEEE Transactions on Vehicular Technology, 2015 | 3 | 2015 |
| Sparse recovery autoencoder X Yu, S Wu, D Holtmann-rice, D Storcheus, S Kumar, A Rostamizadeh US Patent App. 16/442,203, 2019 | 2 | 2019 |
| Information-theoretic study on routing path selection in two-way relay networks S Wu, W Mao, X Wang Globecom Workshops (GC Wkshps), 2013 IEEE, 2013 | 1 | 2013 |
| Prompt Public Large Language Models to Synthesize Data for Private On-device Applications S Wu, Z Xu, Y Zhang, Y Zhang, D Ramage arXiv preprint arXiv:2404.04360, 2024 | | 2024 |
| Profit: Benchmarking Personalization and Robustness Trade-off in Federated Prompt Tuning L Collins, S Wu, S Oh, KC Sim NeurIPS Workshop on Federated Learning in the Age of Foundation Models, 2023, 2023 | | 2023 |
Alexandros G DimakisProfessor, ECE University of Texas at AustinDirección de correo verificada de austin.utexas.edu
