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FedCV: A Federated Learning Framework for Diverse Computer Vision Tasks


Nov 22, 2021
Chaoyang He, Alay Dilipbhai Shah, Zhenheng Tang, Di Fan1Adarshan Naiynar Sivashunmugam, Keerti Bhogaraju, Mita Shimpi, Li Shen, Xiaowen Chu, Mahdi Soltanolkotabi, Salman Avestimehr

* Federated Learning for Computer Vision, an application of FedML Ecosystem (fedml.ai) 

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SSFL: Tackling Label Deficiency in Federated Learning via Personalized Self-Supervision


Oct 06, 2021
Chaoyang He, Zhengyu Yang, Erum Mushtaq, Sunwoo Lee, Mahdi Soltanolkotabi, Salman Avestimehr


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Outlier-Robust Sparse Estimation via Non-Convex Optimization


Sep 23, 2021
Yu Cheng, Ilias Diakonikolas, Daniel M. Kane, Rong Ge, Shivam Gupta, Mahdi Soltanolkotabi


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A Field Guide to Federated Optimization


Jul 14, 2021
Jianyu Wang, Zachary Charles, Zheng Xu, Gauri Joshi, H. Brendan McMahan, Blaise Aguera y Arcas, Maruan Al-Shedivat, Galen Andrew, Salman Avestimehr, Katharine Daly, Deepesh Data, Suhas Diggavi, Hubert Eichner, Advait Gadhikar, Zachary Garrett, Antonious M. Girgis, Filip Hanzely, Andrew Hard, Chaoyang He, Samuel Horvath, Zhouyuan Huo, Alex Ingerman, Martin Jaggi, Tara Javidi, Peter Kairouz, Satyen Kale, Sai Praneeth Karimireddy, Jakub Konecny, Sanmi Koyejo, Tian Li, Luyang Liu, Mehryar Mohri, Hang Qi, Sashank J. Reddi, Peter Richtarik, Karan Singhal, Virginia Smith, Mahdi Soltanolkotabi, Weikang Song, Ananda Theertha Suresh, Sebastian U. Stich, Ameet Talwalkar, Hongyi Wang, Blake Woodworth, Shanshan Wu, Felix X. Yu, Honglin Yuan, Manzil Zaheer, Mi Zhang, Tong Zhang, Chunxiang Zheng, Chen Zhu, Wennan Zhu


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Small random initialization is akin to spectral learning: Optimization and generalization guarantees for overparameterized low-rank matrix reconstruction


Jun 28, 2021
Dominik Stöger, Mahdi Soltanolkotabi

* 80 pages 

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Data augmentation for deep learning based accelerated MRI reconstruction with limited data


Jun 28, 2021
Zalan Fabian, Reinhard Heckel, Mahdi Soltanolkotabi

* 27 pages, 19 figures, to be published in ICML2021 

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Generalization Guarantees for Neural Architecture Search with Train-Validation Split


May 10, 2021
Samet Oymak, Mingchen Li, Mahdi Soltanolkotabi

* to appear in ICML 2021 

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FedNLP: A Research Platform for Federated Learning in Natural Language Processing


Apr 18, 2021
Bill Yuchen Lin, Chaoyang He, Zihang Zeng, Hulin Wang, Yufen Huang, Mahdi Soltanolkotabi, Xiang Ren, Salman Avestimehr

* Github link: https://github.com/FedML-AI/FedNLP 

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Understanding Overparameterization in Generative Adversarial Networks


Apr 12, 2021
Yogesh Balaji, Mohammadmahdi Sajedi, Neha Mukund Kalibhat, Mucong Ding, Dominik Stöger, Mahdi Soltanolkotabi, Soheil Feizi

* Accepted in ICLR 2021 

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PipeTransformer: Automated Elastic Pipelining for Distributed Training of Transformers


Feb 12, 2021
Chaoyang He, Shen Li, Mahdi Soltanolkotabi, Salman Avestimehr


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Theoretical Insights Into Multiclass Classification: A High-dimensional Asymptotic View


Nov 16, 2020
Christos Thrampoulidis, Samet Oymak, Mahdi Soltanolkotabi

* To Appear at NeurIPS 2020. 62 pages, 7 figures 

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Precise Statistical Analysis of Classification Accuracies for Adversarial Training


Oct 21, 2020
Adel Javanmard, Mahdi Soltanolkotabi


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Minimax Lower Bounds for Transfer Learning with Linear and One-hidden Layer Neural Networks


Jun 16, 2020
Seyed Mohammadreza Mousavi Kalan, Zalan Fabian, A. Salman Avestimehr, Mahdi Soltanolkotabi


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Approximation Schemes for ReLU Regression


May 26, 2020
Ilias Diakonikolas, Surbhi Goel, Sushrut Karmalkar, Adam R. Klivans, Mahdi Soltanolkotabi


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Compressive sensing with un-trained neural networks: Gradient descent finds the smoothest approximation


May 07, 2020
Reinhard Heckel, Mahdi Soltanolkotabi

* arXiv admin note: text overlap with arXiv:1910.14634 

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High-Dimensional Robust Mean Estimation via Gradient Descent


May 04, 2020
Yu Cheng, Ilias Diakonikolas, Rong Ge, Mahdi Soltanolkotabi

* Under submission to ICML'20 

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Precise Tradeoffs in Adversarial Training for Linear Regression


Feb 24, 2020
Adel Javanmard, Mahdi Soltanolkotabi, Hamed Hassani


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Convergence and sample complexity of gradient methods for the model-free linear quadratic regulator problem


Dec 26, 2019
Hesameddin Mohammadi, Armin Zare, Mahdi Soltanolkotabi, Mihailo R. Jovanović

* 38 pages, 4 figures 

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Denoising and Regularization via Exploiting the Structural Bias of Convolutional Generators


Oct 31, 2019
Reinhard Heckel, Mahdi Soltanolkotabi


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Generalization Guarantees for Neural Networks via Harnessing the Low-rank Structure of the Jacobian


Jul 04, 2019
Samet Oymak, Zalan Fabian, Mingchen Li, Mahdi Soltanolkotabi


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Gradient Descent with Early Stopping is Provably Robust to Label Noise for Overparameterized Neural Networks


Apr 07, 2019
Mingchen Li, Mahdi Soltanolkotabi, Samet Oymak


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Towards moderate overparameterization: global convergence guarantees for training shallow neural networks


Feb 12, 2019
Samet Oymak, Mahdi Soltanolkotabi


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Fitting ReLUs via SGD and Quantized SGD


Jan 19, 2019
Seyed Mohammadreza Mousavi Kalan, Mahdi Soltanolkotabi, A. Salman Avestimehr


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Overparameterized Nonlinear Learning: Gradient Descent Takes the Shortest Path?


Dec 25, 2018
Samet Oymak, Mahdi Soltanolkotabi


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Polynomially Coded Regression: Optimal Straggler Mitigation via Data Encoding


May 24, 2018
Songze Li, Seyed Mohammadreza Mousavi Kalan, Qian Yu, Mahdi Soltanolkotabi, A. Salman Avestimehr


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End-to-end Learning of a Convolutional Neural Network via Deep Tensor Decomposition


May 16, 2018
Samet Oymak, Mahdi Soltanolkotabi

* 29 pages, 12 figures 

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Fundamental Resource Trade-offs for Encoded Distributed Optimization


Mar 31, 2018
A. Salman Avestimehr, Seyed Mohammadreza Mousavi Kalan, Mahdi Soltanolkotabi


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