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04/10/2023
Interior Point Methods with a Gradient Oracle
We provide an interior point method based on quasi-Newton iterations, wh...
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02/05/2023
Quantized Distributed Training of Large Models with Convergence Guarantees
Communication-reduction techniques are a popular way to improve scalabil...
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11/10/2022
Discrepancy Minimization via Regularization
We introduce a new algorithmic framework for discrepancy minimization ba...
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07/28/2022
CrAM: A Compression-Aware Minimizer
We examine the question of whether SGD-based optimization of deep neural...
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11/19/2021
Faster Sparse Minimum Cost Flow by Electrical Flow Localization
We give an O(m^3/2 - 1/762log (U+W)) time algorithm for minimum cost flo...
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06/23/2021
AC/DC: Alternating Compressed/DeCompressed Training of Deep Neural Networks
The increasing computational requirements of deep neural networks (DNNs)...
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03/05/2021
Decomposable Submodular Function Minimization via Maximum Flow
This paper bridges discrete and continuous optimization approaches for d...
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12/22/2020
Projection-Free Bandit Optimization with Privacy Guarantees
We design differentially private algorithms for the bandit convex optimi...
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07/17/2020
Adaptive Gradient Methods for Constrained Convex Optimization
We provide new adaptive first-order methods for constrained convex optim...
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03/10/2020
Circulation Control for Faster Minimum Cost Flow in Unit-Capacity Graphs
We present an m^11/8+o(1)log W-time algorithm for solving the minimum co...
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02/18/2019
Improved Convergence for ℓ_∞ and ℓ_1 Regression via Iteratively Reweighted Least Squares
The iteratively reweighted least squares method (IRLS) is a popular tech...
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12/04/2018
A Parallel Double Greedy Algorithm for Submodular Maximization
We study parallel algorithms for the problem of maximizing a non-negativ...
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08/29/2018
Submodular Maximization with Packing Constraints in Parallel
We consider the problem of maximizing the multilinear extension of a sub...
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06/19/2017