| Personalized Federated Learning with Theoretical Guarantees: A Model-Agnostic Meta-Learning Approach. A Fallah, A Mokhtari, AE Ozdaglar Advances in Neural Information Processing Systems (NeurIPS) 33, 2020 | 1323* | 2020 |
| Fedpaq: A communication-efficient federated learning method with periodic averaging and quantization A Reisizadeh, A Mokhtari, H Hassani, A Jadbabaie, R Pedarsani International Conference on Artificial Intelligence and Statistics (AISTATS …, 2020 | 762 | 2020 |
| Exploiting Shared Representations for Personalized Federated Learning L Collins, H Hassani, A Mokhtari, S Shakkottai International Conference on Machine Learning (ICML), 2021 | 552 | 2021 |
| A Unified Analysis of Extra-gradient and Optimistic Gradient Methods for Saddle Point Problems: Proximal Point Approach A Mokhtari, A Ozdaglar, S Pattathil International Conference on Artificial Intelligence and Statistics (AISTATS …, 2020 | 342 | 2020 |
| Federated learning with compression: Unified analysis and sharp guarantees F Haddadpour, MM Kamani, A Mokhtari, M Mahdavi International Conference on Artificial Intelligence and Statistics (AISTATS …, 2021 | 259 | 2021 |
| Online Optimization in Dynamic Environments: Improved Regret Rates for Strongly Convex Problems A Mokhtari, S Shahrampour, A Jadbabaie, A Ribeiro Decision and Control (CDC), 2016 IEEE 55th Conference on, 7195-7201, 2016 | 251 | 2016 |
| On the convergence theory of gradient-based model-agnostic meta-learning algorithms A Fallah, A Mokhtari, A Ozdaglar International Conference on Artificial Intelligence and Statistics (AISTATS …, 2020 | 234 | 2020 |
| Global Convergence of Online Limited Memory BFGS A Mokhtari, A Ribeiro Journal of Machine Learning Research 16, 3151-3181, 2015 | 220 | 2015 |
| RES: Regularized Stochastic BFGS Algorithm A Mokhtari, A Ribeiro IEEE Transactions on Signal Processing 62 (23), 6089-6104, 2014 | 196 | 2014 |
| DSA: Decentralized double stochastic averaging gradient algorithm A Mokhtari, A Ribeiro The Journal of Machine Learning Research 17 (1), 2165-2199, 2016 | 192 | 2016 |
| Network Newton Distributed Optimization Methods A Mokhtari, Q Ling, A Ribeiro IEEE Transactions on Signal Processing 65 (1), 146-161, 2017 | 189 | 2017 |
| A Class of Prediction-Correction Methods for Time-Varying Convex Optimization A Simonetto, A Mokhtari, A Koppel, G Leus, A Ribeiro IEEE Transactions on Signal Processing 64 (17), 4576-4591, 2016 | 150 | 2016 |
| Convergence rate of O (1/k) for optimistic gradient and extra-gradient methods in smooth convex-concave saddle point problems A Mokhtari, A Ozdaglar, S Pattathil SIAM Journal on Optimization 30 (4), 3230-3251, 2020 | 140* | 2020 |
| Decentralized Quasi-Newton Methods M Eisen, A Mokhtari, A Ribeiro IEEE Transactions on Signal Processing 65 (10), 2613 - 2628, 2017 | 138 | 2017 |
| DQM: Decentralized Quadratically Approximated Alternating Direction Method of Multipliers A Mokhtari, W Shi, Q Ling, A Ribeiro IEEE Transactions on Signal Processing 64 (19), 5158-5173, 2016 | 136 | 2016 |
| A Decentralized Second-Order Method with Exact Linear Convergence Rate for Consensus Optimization A Mokhtari, W Shi, Q Ling, A Ribeiro IEEE Transactions on Signal and Information Processing over Networks 2 (4 …, 2016 | 135 | 2016 |
| An exact quantized decentralized gradient descent algorithm A Reisizadeh, A Mokhtari, H Hassani, R Pedarsani IEEE Transactions on Signal Processing 67 (19), 4934-4947, 2019 | 129 | 2019 |
| Stochastic conditional gradient methods: From convex minimization to submodular maximization A Mokhtari, H Hassani, A Karbasi Journal of Machine Learning Research 21 (105), 1-49, 2020 | 123 | 2020 |
| Direct Runge-Kutta Discretization Achieves Acceleration J Zhang, A Mokhtari, S Sra, A Jadbabaie Advances in Neural Information Processing Systems (NeurIPS), 2018 | 118 | 2018 |
| Robust and communication-efficient collaborative learning A Reisizadeh, H Taheri, A Mokhtari, H Hassani, R Pedarsani Advances in Neural Information Processing Systems (NeurIPS), 2019 | 104 | 2019 |
Alejandro RibeiroUniversity of Pennsylvania在 seas.upenn.edu 的电子邮件经过验证
