| Federated multi-task learning V Smith, CK Chiang, M Sanjabi, A Talwalkar arXiv preprint arXiv:1705.10467, 2017 | 425 | 2017 |
| Federated learning: Challenges, methods, and future directions T Li, AK Sahu, A Talwalkar, V Smith IEEE Signal Processing Magazine 37 (3), 50-60, 2020 | 345 | 2020 |
| Communication-efficient distributed dual coordinate ascent M Jaggi*, V Smith*, M Takác, J Terhorst, S Krishnan, T Hofmann, ... Advances in Neural Information Processing Systems, 3068-3076, 2014 | 308 | 2014 |
| Federated optimization in heterogeneous networks T Li, AK Sahu, M Zaheer, M Sanjabi, A Talwalkar, V Smith arXiv preprint arXiv:1812.06127, 2018 | 219 | 2018 |
| MLI: An API for distributed machine learning ER Sparks, A Talwalkar, V Smith, J Kottalam, X Pan, J Gonzalez, ... 2013 IEEE 13th International Conference on Data Mining, 1187-1192, 2013 | 198 | 2013 |
| Adding vs. averaging in distributed primal-dual optimization C Ma*, V Smith*, M Jaggi, MI Jordan, P Richtárik, ... International Conference on Machine Learning, 2015 | 158 | 2015 |
| Leaf: A benchmark for federated settings S Caldas, SMK Duddu, P Wu, T Li, J Konečný, HB McMahan, V Smith, ... arXiv preprint arXiv:1812.01097, 2018 | 141 | 2018 |
| CoCoA: A general framework for communication-efficient distributed optimization V Smith, S Forte, M Chenxin, M Takáč, MI Jordan, M Jaggi Journal of Machine Learning Research 18, 230, 2018 | 139 | 2018 |
| Distributed optimization with arbitrary local solvers C Ma, J Konečný, M Jaggi, V Smith, MI Jordan, P Richtárik, M Takáč Optimization Methods and Software 32 (4), 813-848, 2017 | 92 | 2017 |
| Identifying models of HVAC systems using semiparametric regression A Aswani, N Master, J Taneja, V Smith, A Krioukov, D Culler, C Tomlin 2012 American Control Conference (ACC), 3675-3680, 2012 | 86 | 2012 |
| Fair resource allocation in federated learning T Li, M Sanjabi, A Beirami, V Smith arXiv preprint arXiv:1905.10497, 2019 | 82 | 2019 |
| A kernel theory of modern data augmentation T Dao, A Gu, A Ratner, V Smith, C De Sa, C Ré International Conference on Machine Learning, 1528-1537, 2019 | 48 | 2019 |
| One-shot federated learning N Guha, A Talwalkar, V Smith arXiv preprint arXiv:1902.11175, 2019 | 28 | 2019 |
| Federated optimization for heterogeneous networks AK Sahu, T Li, M Sanjabi, M Zaheer, A Talwalkar, V Smith arXiv preprint arXiv:1812.06127 1 (2), 3, 2018 | 25 | 2018 |
| Mlbase: A distributed machine learning wrapper A Talwalkar, T Kraska, R Griffith, J Duchi, J Gonzalez, D Britz, X Pan, ... NIPS Big Learning Workshop, 35, 2012 | 19 | 2012 |
| L1-regularized distributed optimization: A communication-efficient primal-dual framework V Smith, S Forte, MI Jordan, M Jaggi arXiv preprint arXiv:1512.04011, 2015 | 17 | 2015 |
| Modeling building thermal response to HVAC zoning V Smith, T Sookoor, K Whitehouse ACM SIGBED Review 9 (3), 39-45, 2012 | 17 | 2012 |
| Feddane: A federated newton-type method T Li, AK Sahu, M Zaheer, M Sanjabi, A Talwalkar, V Smithy 2019 53rd Asilomar Conference on Signals, Systems, and Computers, 1227-1231, 2019 | 15 | 2019 |
| A comparative study of high renewables penetration electricity grids J Taneja, V Smith, D Culler, C Rosenberg 2013 IEEE International Conference on Smart Grid Communications …, 2013 | 14 | 2013 |
| SysML: The New Frontier of Machine Learning Systems. A Ratner, D Alistarh, G Alonso, DG Andersen, P Bailis, S Bird, N Carlini, ... | 13 | 2019 |
Tian LiCarnegie Mellon UniversityVerified email at cmu.edu
