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05/30/2023
Adaptive Quasi-Newton and Anderson Acceleration Framework with Explicit Global (Accelerated) Convergence Rates
Despite the impressive numerical performance of quasi-Newton and Anderso...
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05/25/2023
SING: A Plug-and-Play DNN Learning Technique
We propose SING (StabIlized and Normalized Gradient), a plug-and-play te...
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09/27/2022
The Curse of Unrolling: Rate of Differentiating Through Optimization
Computing the Jacobian of the solution of an optimization problem is a c...
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06/20/2022
Only Tails Matter: Average-Case Universality and Robustness in the Convex Regime
The recently developed average-case analysis of optimization methods all...
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11/12/2021
Convergence Rates for the MAP of an Exponential Family and Stochastic Mirror Descent – an Open Problem
We consider the problem of upper bounding the expected log-likelihood su...
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06/25/2021
Connecting Sphere Manifolds Hierarchically for Regularization
This paper considers classification problems with hierarchically organiz...
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01/23/2021
Acceleration Methods
This monograph covers some recent advances on a range of acceleration te...
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11/06/2020
Generalization of Quasi-Newton Methods: Application to Robust Symmetric Multisecant Updates
Quasi-Newton techniques approximate the Newton step by estimating the He...
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10/05/2020
Average-case Acceleration for Bilinear Games and Normal Matrices
Advances in generative modeling and adversarial learning have given rise...
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02/12/2020
Average-case Acceleration Through Spectral Density Estimation
We develop a framework for designing optimal quadratic optimization meth...
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01/02/2020
Accelerating Smooth Games by Manipulating Spectral Shapes
We use matrix iteration theory to characterize acceleration in smooth ga...
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05/29/2019
Extra-gradient with player sampling for provable fast convergence in n-player games
Data-driven model training is increasingly relying on finding Nash equil...
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06/01/2018
Nonlinear Acceleration of CNNs
The Regularized Nonlinear Acceleration (RNA) algorithm is an acceleratio...
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05/24/2018