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Efficient and Modular Implicit Differentiation

May 31, 2021
Mathieu Blondel, Quentin Berthet, Marco Cuturi, Roy Frostig, Stephan Hoyer, Felipe Llinares-López, Fabian Pedregosa, Jean-Philippe Vert

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Decomposing reverse-mode automatic differentiation

May 20, 2021
Roy Frostig, Matthew J. Johnson, Dougal Maclaurin, Adam Paszke, Alexey Radul

* Presented at the LAFI 2021 workshop at POPL, 17 January 2021 

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The advantages of multiple classes for reducing overfitting from test set reuse

May 24, 2019
Vitaly Feldman, Roy Frostig, Moritz Hardt

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Measuring the Effects of Data Parallelism on Neural Network Training

Nov 21, 2018
Christopher J. Shallue, Jaehoon Lee, Joseph Antognini, Jascha Sohl-Dickstein, Roy Frostig, George E. Dahl

* Submitted to JMLR 

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Toward Deeper Understanding of Neural Networks: The Power of Initialization and a Dual View on Expressivity

May 19, 2017
Amit Daniely, Roy Frostig, Yoram Singer

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Random Features for Compositional Kernels

Mar 22, 2017
Amit Daniely, Roy Frostig, Vineet Gupta, Yoram Singer

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Estimation from Indirect Supervision with Linear Moments

Aug 10, 2016
Aditi Raghunathan, Roy Frostig, John Duchi, Percy Liang

* 12 pages, 7 figures, extended and updated version of our paper appearing in ICML 2016 

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Principal Component Projection Without Principal Component Analysis

Feb 22, 2016
Roy Frostig, Cameron Musco, Christopher Musco, Aaron Sidford

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Un-regularizing: approximate proximal point and faster stochastic algorithms for empirical risk minimization

Jun 24, 2015
Roy Frostig, Rong Ge, Sham M. Kakade, Aaron Sidford

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Competing with the Empirical Risk Minimizer in a Single Pass

Feb 25, 2015
Roy Frostig, Rong Ge, Sham M. Kakade, Aaron Sidford

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Relaxations for inference in restricted Boltzmann machines

Jan 02, 2014
Sida I. Wang, Roy Frostig, Percy Liang, Christopher D. Manning

* ICLR 2014 workshop track submission 

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