| 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, 2021-2031, 2020 | 886 | 2020 |
| Exploiting shared representations for personalized federated learning L Collins, H Hassani, A Mokhtari, S Shakkottai International conference on machine learning, 2089-2099, 2021 | 764 | 2021 |
| Efficient and accurate estimation of lipschitz constants for deep neural networks M Fazlyab, A Robey, H Hassani, M Morari, G Pappas Advances in neural information processing systems 32, 2019 | 523 | 2019 |
| Jailbreaking black box large language models in twenty queries P Chao, A Robey, E Dobriban, H Hassani, GJ Pappas, E Wong arXiv preprint arXiv:2310.08419, 2023 | 366 | 2023 |
| Fast and provably good seedings for k-means O Bachem, M Lucic, H Hassani, A Krause Advances in neural information processing systems 29, 2016 | 195 | 2016 |
| Linear convergence in federated learning: Tackling client heterogeneity and sparse gradients A Mitra, R Jaafar, GJ Pappas, H Hassani Advances in Neural Information Processing Systems 34, 14606-14619, 2021 | 189* | 2021 |
| Finite-length scaling of polar codes SH Hassani, K Alishahi, R Urbanke arXiv preprint arXiv:1304.4778, 2013 | 186* | 2013 |
| Smoothllm: Defending large language models against jailbreaking attacks A Robey, E Wong, H Hassani, GJ Pappas arXiv preprint arXiv:2310.03684, 2023 | 182 | 2023 |
| 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 | 176* | 2019 |
| Approximate k-means++ in sublinear time O Bachem, M Lucic, SH Hassani, A Krause Proceedings of the AAAI conference on artificial intelligence 30 (1), 2016 | 176 | 2016 |
| Age of information in random access channels X Chen, K Gatsis, H Hassani, SS Bidokhti IEEE Transactions on Information Theory 68 (10), 6548-6568, 2022 | 172 | 2022 |
| Unified scaling of polar codes: Error exponent, scaling exponent, moderate deviations, and error floors M Mondelli, SH Hassani, RL Urbanke IEEE Transactions on Information Theory 62 (12), 6698-6712, 2016 | 150 | 2016 |
| From polar to Reed-Muller codes: A technique to improve the finite-length performance M Mondelli, SH Hassani, RL Urbanke IEEE Transactions on Communications 62 (9), 3084-3091, 2014 | 150 | 2014 |
| On the construction of polar codes R Pedarsani, SH Hassani, I Tal, E Telatar Information Theory Proceedings (ISIT), 2011 IEEE International Symposium on …, 2011 | 150 | 2011 |
| Gradient methods for submodular maximization H Hassani, M Soltanolkotabi, A Karbasi Advances in Neural Information Processing Systems 30, 2017 | 147 | 2017 |
| Model-based domain generalization A Robey, GJ Pappas, H Hassani Advances in Neural Information Processing Systems 34, 20210-20229, 2021 | 142 | 2021 |
| Growing a graph matching from a handful of seeds E Kazemi, SH Hassani, M Grossglauser Proceedings of the VLDB Endowment 8 (10), 1010-1021, 2015 | 139 | 2015 |
| 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 | 133 | 2020 |
| Straggler-resilient federated learning: Leveraging the interplay between statistical accuracy and system heterogeneity A Reisizadeh, I Tziotis, H Hassani, A Mokhtari, R Pedarsani IEEE Journal on Selected Areas in Information Theory 3 (2), 197-205, 2022 | 126 | 2022 |
| Precise tradeoffs in adversarial training for linear regression A Javanmard, M Soltanolkotabi, H Hassani Conference on Learning Theory, 2034-2078, 2020 | 124 | 2020 |
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