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Regularized linear autoencoders recover the principal components, eventually

Jul 13, 2020
Xuchan Bao, James Lucas, Sushant Sachdeva, Roger Grosse


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Faster Graph Embeddings via Coarsening

Jul 06, 2020
Matthew Fahrbach, Gramoz Goranci, Richard Peng, Sushant Sachdeva, Chi Wang

* Proceedings of the 37th International Conference on Machine Learning (ICML 2020) 
* 18 pages, 2 figures 

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A Provably Convergent and Practical Algorithm for Min-max Optimization with Applications to GANs

Jun 23, 2020
Oren Mangoubi, Sushant Sachdeva, Nisheeth K. Vishnoi


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Fast, Provably convergent IRLS Algorithm for p-norm Linear Regression

Jul 16, 2019
Deeksha Adil, Richard Peng, Sushant Sachdeva

* Code for this work is available at https://github.com/utoronto-theory/pIRLS 

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Which Algorithmic Choices Matter at Which Batch Sizes? Insights From a Noisy Quadratic Model

Jul 09, 2019
Guodong Zhang, Lala Li, Zachary Nado, James Martens, Sushant Sachdeva, George E. Dahl, Christopher J. Shallue, Roger Grosse


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Iterative Refinement for $\ell_p$-norm Regression

Jan 21, 2019
Deeksha Adil, Rasmus Kyng, Richard Peng, Sushant Sachdeva

* Published in SODA 2019. Was initially submitted to SODA on July 12, 2018 

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Convergence Results for Neural Networks via Electrodynamics

Nov 21, 2017
Rina Panigrahy, Ali Rahimi, Sushant Sachdeva, Qiuyi Zhang

* in ITCS 2018 

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Fast, Provable Algorithms for Isotonic Regression in all $\ell_{p}$-norms

Nov 11, 2015
Rasmus Kyng, Anup Rao, Sushant Sachdeva


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Algorithms for Lipschitz Learning on Graphs

Jun 30, 2015
Rasmus Kyng, Anup Rao, Sushant Sachdeva, Daniel A. Spielman

* Code used in this work is available at https://github.com/danspielman/YINSlex 30 pages 

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Provable ICA with Unknown Gaussian Noise, and Implications for Gaussian Mixtures and Autoencoders

Nov 12, 2012
Sanjeev Arora, Rong Ge, Ankur Moitra, Sushant Sachdeva


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