
Active strict saddles in nonsmooth optimization
We introduce a geometrically transparent strict saddle property for nons...
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Robust stochastic optimization with the proximal point method
Standard results in stochastic convex optimization bound the number of s...
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Stochastic algorithms with geometric step decay converge linearly on sharp functions
Stochastic (sub)gradient methods require step size schedule tuning to pe...
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Lowrank matrix recovery with composite optimization: good conditioning and rapid convergence
The task of recovering a lowrank matrix from its noisy linear measureme...
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Composite optimization for robust blind deconvolution
The blind deconvolution problem seeks to recover a pair of vectors from ...
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Uniform Graphical Convergence of Subgradients in Nonconvex Optimization and Learning
We investigate the stochastic optimization problem of minimizing populat...
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Stochastic modelbased minimization under highorder growth
Given a nonsmooth, nonconvex minimization problem, we consider algorithm...
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Stochastic subgradient method converges on tame functions
This work considers the question: what convergence guarantees does the s...
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Stochastic modelbased minimization of weakly convex functions
We consider an algorithm that successively samples and minimizes stochas...
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Stochastic subgradient method converges at the rate O(k^1/4) on weakly convex functions
We prove that the projected stochastic subgradient method, applied to a ...
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A SMART Stochastic Algorithm for Nonconvex Optimization with Applications to Robust Machine Learning
In this paper, we show how to transform any optimization problem that ar...
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Beating levelset methods for 3D seismic data interpolation: a primaldual alternating approach
Acquisition cost is a crucial bottleneck for seismic workflows, and low...
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On the Design and Analysis of Multiple View Descriptors
We propose an extension of popular descriptors based on gradient orienta...
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Damek Davis
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