| Foundations of Machine Learning M Mohri, A Rostamizadeh, A Talwalkar Cambridge, MA: MIT Press, 2018 | 6860 | 2018 |
| Federated optimization in heterogeneous networks T Li, AK Sahu, M Zaheer, M Sanjabi, A Talwalkar, V Smith Proceedings of Machine learning and systems 2, 429-450, 2020 | 5560 | 2020 |
| Federated learning: Challenges, methods, and future directions T Li, AK Sahu, A Talwalkar, V Smith IEEE signal processing magazine 37 (3), 50-60, 2020 | 5329 | 2020 |
| Hyperband: A novel bandit-based approach to hyperparameter optimization L Li, K Jamieson, G DeSalvo, A Rostamizadeh, A Talwalkar arXiv preprint arXiv:1603.06560, 2016 | 2981 | 2016 |
| Mllib: Machine learning in apache spark X Meng, J Bradley, B Yavuz, E Sparks, S Venkataraman, D Liu, ... Journal of Machine Learning Research 17 (34), 1-7, 2016 | 2462 | 2016 |
| Federated multi-task learning V Smith, CK Chiang, M Sanjabi, AS Talwalkar Advances in neural information processing systems 30, 2017 | 2152 | 2017 |
| 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 | 1478 | 2018 |
| A large-scale evaluation of computational protein function prediction P Radivojac, WT Clark, TR Oron, AM Schnoes, T Wittkop, A Sokolov, ... Nature methods 10 (3), 221-227, 2013 | 1103 | 2013 |
| Random search and reproducibility for neural architecture search L Li, A Talwalkar Uncertainty in artificial intelligence, 367-377, 2020 | 824 | 2020 |
| Non-stochastic best arm identification and hyperparameter optimization K Jamieson, A Talwalkar Artificial intelligence and statistics, 240-248, 2016 | 743 | 2016 |
| A scalable bootstrap for massive data A Kleiner, A Talwalkar, P Sarkar, MI Jordan Journal of the Royal Statistical Society, Series B, 2013 | 501 | 2013 |
| Expanding the reach of federated learning by reducing client resource requirements S Caldas, J Konečny, HB McMahan, A Talwalkar arXiv preprint arXiv:1812.07210, 2018 | 490 | 2018 |
| MLbase: A Distributed Machine-learning System. T Kraska, A Talwalkar, JC Duchi, R Griffith, MJ Franklin, MI Jordan Cidr 1, 2-1, 2013 | 482 | 2013 |
| Sampling methods for the Nyström method S Kumar, M Mohri, A Talwalkar The Journal of Machine Learning Research 13 (1), 981-1006, 2012 | 473 | 2012 |
| A system for massively parallel hyperparameter tuning L Li, K Jamieson, A Rostamizadeh, E Gonina, J Ben-Tzur, M Hardt, ... Proceedings of Machine Learning and Systems 2, 230-246, 2020 | 464 | 2020 |
| Adaptive gradient-based meta-learning methods M Khodak, MFF Balcan, AS Talwalkar Advances in Neural Information Processing Systems 32, 2019 | 394 | 2019 |
| A field guide to federated optimization J Wang, Z Charles, Z Xu, G Joshi, HB McMahan, M Al-Shedivat, G Andrew, ... arXiv preprint arXiv:2107.06917, 2021 | 379 | 2021 |
| Large-scale manifold learning A Talwalkar, S Kumar, H Rowley 2008 IEEE Conference on Computer Vision and Pattern Recognition, 1-8, 2008 | 274 | 2008 |
| Model agnostic supervised local explanations G Plumb, D Molitor, AS Talwalkar Advances in neural information processing systems 31, 2018 | 252 | 2018 |
| Divide-and-Conquer Matrix Factorization LW Mackey, A Talwalkar, MI Jordan NIPS, 1134-1142, 2011 | 249 | 2011 |
Virginia SmithCarnegie Mellon University在 cmu.edu 的电子邮件经过验证
