Damek Davis

Found. Comput. Math., June, 2025

Iteratively reweighted kernel machines efficiently learn sparse functions.

[BibT_eX]

[DOI]

CoRR, May, 2025

Stochastic Optimization Over Proximally Smooth Sets.

[BibT_eX]

[DOI]

Zhan Shi

SIAM J. Optim., 2025

Online Covariance Estimation in Nonsmooth Stochastic Approximation.

[BibT_eX]

[DOI]

Abhishek Roy

Krishna Balasubramanian

Sen Na

Proceedings of the Thirty Eighth Annual Conference on Learning Theory, 2025

2024

Global Optimality of the EM Algorithm for Mixtures of Two-Component Linear Regressions.

[BibT_eX]

[DOI]

Jeongyeol Kwon

Wei Qian

Yudong Chen

Constantine Caramanis

Nhat Ho

IEEE Trans. Inf. Theory, September, 2024

Stochastic algorithms with geometric step decay converge linearly on sharp functions.

[BibT_eX]

[DOI]

Math. Program., September, 2024

A Superlinearly Convergent Subgradient Method for Sharp Semismooth Problems.

[BibT_eX]

[DOI]

Math. Oper. Res., 2024

Gradient descent with adaptive stepsize converges (nearly) linearly under fourth-order growth.

[BibT_eX]

[DOI]

CoRR, 2024

2023

Special Issue: International Symposium on Mathematical Programming 2022.

[BibT_eX]

[DOI]

Math. Program., July, 2023

Variance reduction for root-finding problems.

[BibT_eX]

[DOI]

Math. Program., January, 2023

Aiming towards the minimizers: fast convergence of SGD for overparametrized problems.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022

Escaping Strict Saddle Points of the Moreau Envelope in Nonsmooth Optimization.

[BibT_eX]

[DOI]

Mateo Díaz

SIAM J. Optim., September, 2022

Graphical Convergence of Subgradients in Nonconvex Optimization and Learning.

[BibT_eX]

[DOI]

Math. Oper. Res., 2022

Proximal Methods Avoid Active Strict Saddles of Weakly Convex Functions.

[BibT_eX]

[DOI]

Found. Comput. Math., 2022

A gradient sampling method with complexity guarantees for Lipschitz functions in high and low dimensions.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021

From Low Probability to High Confidence in Stochastic Convex Optimization.

[BibT_eX]

[DOI]

J. Mach. Learn. Res., 2021

Low-Rank Matrix Recovery with Composite Optimization: Good Conditioning and Rapid Convergence.

[BibT_eX]

[DOI]

Found. Comput. Math., 2021

Clustering a Mixture of Gaussians with Unknown Covariance.

[BibT_eX]

[DOI]

Mateo Díaz

Kaizheng Wang

CoRR, 2021

Subgradient methods near active manifolds: saddle point avoidance, local convergence, and asymptotic normality.

[BibT_eX]

[DOI]

CoRR, 2021

2020

Trimmed Statistical Estimation via Variance Reduction.

[BibT_eX]

[DOI]

Aleksandr Y. Aravkin

Math. Oper. Res., 2020

Stochastic Subgradient Method Converges on Tame Functions.

[BibT_eX]

[DOI]

Found. Comput. Math., 2020

High probability guarantees for stochastic convex optimization.

[BibT_eX]

[DOI]

Proceedings of the Conference on Learning Theory, 2020

2019

Proximally Guided Stochastic Subgradient Method for Nonsmooth, Nonconvex Problems.

[BibT_eX]

[DOI]

Benjamin Grimmer

SIAM J. Optim., 2019

Stochastic Model-Based Minimization of Weakly Convex Functions.

[BibT_eX]

[DOI]

SIAM J. Optim., 2019

Active strict saddles in nonsmooth optimization.

[BibT_eX]

[DOI]

CoRR, 2019

Robust stochastic optimization with the proximal point method.

[BibT_eX]

[DOI]

CoRR, 2019

Composite optimization for robust blind deconvolution.

[BibT_eX]

[DOI]

Mateo Díaz

CoRR, 2019

Global Convergence of the EM Algorithm for Mixtures of Two Component Linear Regression.

[BibT_eX]

[DOI]

Jeongyeol Kwon

Wei Qian

Constantine Caramanis

Yudong Chen

Proceedings of the Conference on Learning Theory, 2019

2018

Forward-Backward-Half Forward Algorithm for Solving Monotone Inclusions.

[BibT_eX]

[DOI]

Luis M. Briceño-Arias

SIAM J. Optim., 2018

Subgradient Methods for Sharp Weakly Convex Functions.

[BibT_eX]

[DOI]

J. Optim. Theory Appl., 2018

Uniform Graphical Convergence of Subgradients in Nonconvex Optimization and Learning.

[BibT_eX]

[DOI]

CoRR, 2018

Stochastic model-based minimization under high-order growth.

[BibT_eX]

[DOI]

Kellie J. MacPhee

CoRR, 2018

Stochastic subgradient method converges at the rate O(k<sup>-1/4</sup>) on weakly convex functions.

[BibT_eX]

[DOI]

CoRR, 2018

2017

Beating Level-Set Methods for 5-D Seismic Data Interpolation: A Primal-Dual Alternating Approach.

[BibT_eX]

[DOI]

IEEE Trans. Computational Imaging, 2017

Faster Convergence Rates of Relaxed Peaceman-Rachford and ADMM Under Regularity Assumptions.

[BibT_eX]

[DOI]

Wotao Yin

Math. Oper. Res., 2017

2016

A SMART Stochastic Algorithm for Nonconvex Optimization with Applications to Robust Machine Learning.

[BibT_eX]

[DOI]

Aleksandr Y. Aravkin

CoRR, 2016

The Sound of APALM Clapping: Faster Nonsmooth Nonconvex Optimization with Stochastic Asynchronous PALM.

[BibT_eX]

[DOI]

Brent Edmunds

Madeleine Udell

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

2015

Convergence Rate Analysis of Primal-Dual Splitting Schemes.

[BibT_eX]

[DOI]

SIAM J. Optim., 2015

Convergence Rate Analysis of the Forward-Douglas-Rachford Splitting Scheme.

[BibT_eX]

[DOI]

SIAM J. Optim., 2015

An O(nlog(n)) Algorithm for Projecting Onto the Ordered Weighted ℓ<sub>1</sub> Norm Ball.

[BibT_eX]

[DOI]

CoRR, 2015

Multi-view feature engineering and learning.

[BibT_eX]

[DOI]

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015

2014

Tactical Scheduling for Precision Air Traffic Operations: Past Research and Current Problems.

[BibT_eX]

[DOI]

Douglas R. Isaacson

Alexander V. Sadovsky

J. Aerosp. Inf. Syst., 2014

Asymmetric Sparse Kernel Approximations for Large-Scale Visual Search.

[BibT_eX]

[DOI]