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Hamed Hassani
Hamed Hassani
Electrical Engineering, Computer Science, and Statistics; University of Pennsylvania
Verified email at seas.upenn.edu - Homepage
Title
Cited by
Cited by
Year
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
6812020
Exploiting shared representations for personalized federated learning
L Collins, H Hassani, A Mokhtari, S Shakkottai
International conference on machine learning, 2089-2099, 2021
4462021
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
4162019
Fast and provably good seedings for k-means
O Bachem, M Lucic, H Hassani, A Krause
Advances in neural information processing systems 29, 2016
1872016
Finite-length scaling of polar codes
SH Hassani, K Alishahi, R Urbanke
arXiv preprint arXiv:1304.4778, 2013
181*2013
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
1682016
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
151*2019
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
1442014
On the construction of polar codes
R Pedarsani, SH Hassani, I Tal, E Telatar
Information Theory Proceedings (ISIT), 2011 IEEE International Symposium on …, 2011
1442011
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
1432016
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
139*2021
Gradient methods for submodular maximization
H Hassani, M Soltanolkotabi, A Karbasi
Advances in Neural Information Processing Systems 30, 2017
1362017
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
1362015
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
1252022
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
1162020
Model-based domain generalization
A Robey, GJ Pappas, H Hassani
Advances in Neural Information Processing Systems 34, 20210-20229, 2021
1082021
Precise tradeoffs in adversarial training for linear regression
A Javanmard, M Soltanolkotabi, H Hassani
Conference on Learning Theory, 2034-2078, 2020
1062020
Universal polar codes
SH Hassani, R Urbanke
2014 IEEE International Symposium on Information Theory, 1451-1455, 2014
1022014
Robust and communication-efficient collaborative learning
A Reisizadeh, H Taheri, A Mokhtari, H Hassani, R Pedarsani
Advances in Neural Information Processing Systems 32, 2019
1002019
How to achieve the capacity of asymmetric channels
M Mondelli, R Urbanke, SH Hassani
2014 52nd Annual Allerton Conference on Communication, Control, and …, 2014
98*2014
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Articles 1–20