| Federated Optimization in Heterogeneous Networks T Li, AK Sahu, M Zaheer, M Sanjabi, A Talwalkar, V Smith arXiv preprint arXiv:1812.06127, 2018 | 6370 | 2018 |
| Federated multi-task learning V Smith, CK Chiang, M Sanjabi, AS Talwalkar Advances in neural information processing systems 30, 2017 | 2329 | 2017 |
| Fair resource allocation in federated learning T Li, M Sanjabi, A Beirami, V Smith arXiv preprint arXiv:1905.10497, 2019 | 1012 | 2019 |
| Solving a class of non-convex min-max games using iterative first order methods M Nouiehed, M Sanjabi, T Huang, JD Lee, M Razaviyayn Advances in Neural Information Processing Systems 32, 2019 | 406 | 2019 |
| Linear Transceiver Design for Interference Alignment: Complexity and Computation M Razaviyayn, M Sanjabi, ZQ Luo IEEE Transactions on Information Theory ( Volume: 58 , Issue: 5 , May 2012 …, 2012 | 203 | 2012 |
| Linear transceiver design for interference alignment: Complexity and computation M Razaviyayn, M Sanjabi Boroujeni, ZQ Luo Signal Processing Advances in Wireless Communications (SPAWC), 2010 IEEE …, 2010 | 203 | 2010 |
| 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 | 196 | 2019 |
| Federated learning with partial model personalization K Pillutla, K Malik, AR Mohamed, M Rabbat, M Sanjabi, L Xiao International Conference on Machine Learning, 17716-17758, 2022 | 188 | 2022 |
| On the Convergence and Robustness of Training GANs with Regularized Optimal Transport M Sanjabi, B Jimmy, M Razaviyayn, JD Lee Advances in Neural Information Processing Systems, 7088--7098, 2018 | 186 | 2018 |
| Tilted empirical risk minimization T Li, A Beirami, M Sanjabi, V Smith arXiv preprint arXiv:2007.01162, 2020 | 158 | 2020 |
| Nonconvex min-max optimization: Applications, challenges, and recent theoretical advances M Razaviyayn, T Huang, S Lu, M Nouiehed, M Sanjabi, M Hong IEEE Signal Processing Magazine 37 (5), 55-66, 2020 | 133 | 2020 |
| Where to begin? on the impact of pre-training and initialization in federated learning J Nguyen, J Wang, K Malik, M Sanjabi, M Rabbat arXiv preprint arXiv:2206.15387, 2022 | 124 | 2022 |
| A stochastic successive minimization method for nonsmooth nonconvex optimization with applications to transceiver design in wireless communication networks M Razaviyayn, M Sanjabi, ZQ Luo Mathematical Programming 157, 515-545, 2016 | 114 | 2016 |
| Robust SINR-constrained MISO downlink beamforming: When is semidefinite programming relaxation tight? E Song, Q Shi, M Sanjabi, RY Sun, ZQ Luo EURASIP Journal on Wireless Communications and Networking 2012, 1-11, 2012 | 98 | 2012 |
| Optimal joint base station assignment and beamforming for heterogeneous networks M Sanjabi, M Razaviyayn, ZQ Luo IEEE Transactions on Signal Processing 62 (8), 1950-1961, 2014 | 82 | 2014 |
| Accelerated alternating direction method of multipliers M Kadkhodaie, K Christakopoulou, M Sanjabi, A Banerjee Proceedings of the 21th ACM SIGKDD international conference on knowledge …, 2015 | 72 | 2015 |
| Cross-layer provision of future cellular networks: A WMMSE-based approach H Baligh, M Hong, WC Liao, ZQ Luo, M Razaviyayn, M Sanjabi, R Sun IEEE Signal Processing Magazine 31 (6), 56-68, 2014 | 56 | 2014 |
| On tilted losses in machine learning: Theory and applications T Li, A Beirami, M Sanjabi, V Smith Journal of Machine Learning Research 24 (142), 1-79, 2023 | 51 | 2023 |
| Unirex: A unified learning framework for language model rationale extraction A Chan, M Sanjabi, L Mathias, L Tan, S Nie, X Peng, X Ren, H Firooz International Conference on Machine Learning, 2867-2889, 2022 | 46 | 2022 |
| Text-to-concept (and back) via cross-model alignment M Moayeri, K Rezaei, M Sanjabi, S Feizi International Conference on Machine Learning, 25037-25060, 2023 | 44 | 2023 |
Virginia SmithCarnegie Mellon UniversityE-mail megerősítve itt: cmu.edu