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09/06/2023
Adaptive Consensus: A network pruning approach for decentralized optimization
We consider network-based decentralized optimization problems, where eac...
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06/15/2022
On the fast convergence of minibatch heavy ball momentum
Simple stochastic momentum methods are widely used in machine learning o...
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09/24/2021
Adaptive Sampling Quasi-Newton Methods for Zeroth-Order Stochastic Optimization
We consider unconstrained stochastic optimization problems with no avail...
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03/07/2021
Retrospective Approximation for Smooth Stochastic Optimization
We consider stochastic optimization problems where a smooth (and potenti...
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12/31/2020
Constrained and Composite Optimization via Adaptive Sampling Methods
The motivation for this paper stems from the desire to develop an adapti...
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10/29/2019
Adaptive Sampling Quasi-Newton Methods for Derivative-Free Stochastic Optimization
We consider stochastic zero-order optimization problems, which arise in ...
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02/15/2018
A Progressive Batching L-BFGS Method for Machine Learning
The standard L-BFGS method relies on gradient approximations that are no...
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10/30/2017
Adaptive Sampling Strategies for Stochastic Optimization
In this paper, we propose a stochastic optimization method that adaptive...
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05/17/2017
An Investigation of Newton-Sketch and Subsampled Newton Methods
The concepts of sketching and subsampling have recently received much at...
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09/27/2016
