On the Design and Analysis of Multiple View Descriptors. - Jingming Dong; Jonathan Balzer; Damek Davis; Joshua Hernandez; Stefano Soatto (CoRR, 2013)

Asymmetric Sparse Kernel Approximations for Large-Scale Visual Search. - Damek Davis; Jonathan Balzer; Stefano Soatto (CVPR, 2014)

Tactical Scheduling for Precision Air Traffic Operations: Past Research and Current Problems. - Douglas R. Isaacson; Alexander V. Sadovsky; Damek Davis (J. Aerosp. Inf. Syst., 2014)

Multi-view feature engineering and learning. - Jingming Dong; Nikolaos Karianakis; Damek Davis; Joshua Hernandez; Jonathan Balzer; Stefano Soatto (CVPR, 2015)

An O(nlog(n)) Algorithm for Projecting Onto the Ordered Weighted ℓ1 Norm Ball. - Damek Davis (CoRR, 2015)

Convergence Rate Analysis of the Forward-Douglas-Rachford Splitting Scheme. - Damek Davis (SIAM J. Optim., 2015)

Convergence Rate Analysis of Primal-Dual Splitting Schemes. - Damek Davis (SIAM J. Optim., 2015)

The Sound of APALM Clapping: Faster Nonsmooth Nonconvex Optimization with Stochastic Asynchronous PALM. - Damek Davis; Brent Edmunds; Madeleine Udell (NIPS, 2016)

A SMART Stochastic Algorithm for Nonconvex Optimization with Applications to Robust Machine Learning. - Aleksandr Y. Aravkin; Damek Davis (CoRR, 2016)

Faster Convergence Rates of Relaxed Peaceman-Rachford and ADMM Under Regularity Assumptions. - Damek Davis; Wotao Yin (Math. Oper. Res., 2017)

Beating Level-Set Methods for 5-D Seismic Data Interpolation: A Primal-Dual Alternating Approach. - Rajiv Kumar 003; Oscar López; Damek Davis; Aleksandr Y. Aravkin; Felix J. Herrmann (IEEE Trans. Computational Imaging, 2017)

Stochastic subgradient method converges at the rate O(k-1/4) on weakly convex functions. - Damek Davis; Dmitriy Drusvyatskiy (CoRR, 2018)

Stochastic model-based minimization under high-order growth. - Damek Davis; Dmitriy Drusvyatskiy; Kellie J. MacPhee (CoRR, 2018)

Uniform Graphical Convergence of Subgradients in Nonconvex Optimization and Learning. - Damek Davis; Dmitriy Drusvyatskiy (CoRR, 2018)

Subgradient Methods for Sharp Weakly Convex Functions. - Damek Davis; Dmitriy Drusvyatskiy; Kellie J. MacPhee; Courtney Paquette (J. Optim. Theory Appl., 2018)

Forward-Backward-Half Forward Algorithm for Solving Monotone Inclusions. - Luis M. Briceño-Arias; Damek Davis (SIAM J. Optim., 2018)

Global Convergence of the EM Algorithm for Mixtures of Two Component Linear Regression. - Jeongyeol Kwon; Wei Qian; Constantine Caramanis; Yudong Chen 001; Damek Davis (COLT, 2019)

Composite optimization for robust blind deconvolution. - Vasileios Charisopoulos; Damek Davis; Mateo Díaz; Dmitriy Drusvyatskiy (CoRR, 2019)

Robust stochastic optimization with the proximal point method. - Damek Davis; Dmitriy Drusvyatskiy (CoRR, 2019)

Active strict saddles in nonsmooth optimization. - Damek Davis; Dmitriy Drusvyatskiy (CoRR, 2019)

Stochastic Model-Based Minimization of Weakly Convex Functions. - Damek Davis; Dmitriy Drusvyatskiy (SIAM J. Optim., 2019)

Proximally Guided Stochastic Subgradient Method for Nonsmooth, Nonconvex Problems. - Damek Davis; Benjamin Grimmer (SIAM J. Optim., 2019)

High probability guarantees for stochastic convex optimization. - Damek Davis; Dmitriy Drusvyatskiy (COLT, 2020)

Stochastic Subgradient Method Converges on Tame Functions. - Damek Davis; Dmitriy Drusvyatskiy; Sham M. Kakade; Jason D. Lee (Found. Comput. Math., 2020)

Trimmed Statistical Estimation via Variance Reduction. - Aleksandr Y. Aravkin; Damek Davis (Math. Oper. Res., 2020)

Subgradient methods near active manifolds: saddle point avoidance, local convergence, and asymptotic normality. - Damek Davis; Dmitriy Drusvyatskiy; Liwei Jiang (CoRR, 2021)

Clustering a Mixture of Gaussians with Unknown Covariance. - Damek Davis; Mateo Díaz; Kaizheng Wang (CoRR, 2021)

Low-Rank Matrix Recovery with Composite Optimization: Good Conditioning and Rapid Convergence. - Vasileios Charisopoulos; Yudong Chen 001; Damek Davis; Mateo Díaz; Lijun Ding; Dmitriy Drusvyatskiy (Found. Comput. Math., 2021)

From Low Probability to High Confidence in Stochastic Convex Optimization. - Damek Davis; Dmitriy Drusvyatskiy; Lin Xiao 003; Junyu Zhang (J. Mach. Learn. Res., 2021)

A gradient sampling method with complexity guarantees for Lipschitz functions in high and low dimensions. - Damek Davis; Dmitriy Drusvyatskiy; Yin Tat Lee; Swati Padmanabhan; Guanghao Ye (NeurIPS, 2022)

Proximal Methods Avoid Active Strict Saddles of Weakly Convex Functions. - Damek Davis; Dmitriy Drusvyatskiy (Found. Comput. Math., 2022)

Graphical Convergence of Subgradients in Nonconvex Optimization and Learning. - Damek Davis; Dmitriy Drusvyatskiy (Math. Oper. Res., 2022)

Escaping Strict Saddle Points of the Moreau Envelope in Nonsmooth Optimization. - Damek Davis; Mateo Díaz; Dmitriy Drusvyatskiy (SIAM J. Optim., 2022)

Aiming towards the minimizers: fast convergence of SGD for overparametrized problems. - Chaoyue Liu 001; Dmitriy Drusvyatskiy; Mikhail Belkin; Damek Davis; Yi-An Ma (NeurIPS, 2023)

Variance reduction for root-finding problems. - Damek Davis (Math. Program., 2023)

Special Issue: International Symposium on Mathematical Programming 2022. - Damek Shea Davis; Oktay Günlük; Volker Kaibel; Giacomo Nannicini; Xa-Xiang Yuan (Math. Program., 2023)

Gradient descent with adaptive stepsize converges (nearly) linearly under fourth-order growth. - Damek Davis; Dmitriy Drusvyatskiy; Liwei Jiang (CoRR, 2024)

A Superlinearly Convergent Subgradient Method for Sharp Semismooth Problems. - Vasileios Charisopoulos; Damek Davis (Math. Oper. Res., 2024)

Stochastic algorithms with geometric step decay converge linearly on sharp functions. - Damek Davis; Dmitriy Drusvyatskiy; Vasileios Charisopoulos (Math. Program., 2024)

Global Optimality of the EM Algorithm for Mixtures of Two-Component Linear Regressions. - Jeongyeol Kwon; Wei Qian; Yudong Chen 001; Constantine Caramanis; Damek Davis; Nhat Ho (IEEE Trans. Inf. Theory, 2024)

Online Covariance Estimation in Nonsmooth Stochastic Approximation. - Liwei Jiang; Abhishek Roy; Krishna Balasubramanian; Damek Davis; Dmitriy Drusvyatskiy; Sen Na (COLT, 2025)

Stochastic Optimization Over Proximally Smooth Sets. - Damek Davis; Dmitriy Drusvyatskiy; Zhan Shi (SIAM J. Optim., 2025)

Iteratively reweighted kernel machines efficiently learn sparse functions. - Libin Zhu; Damek Davis; Dmitriy Drusvyatskiy; Maryam Fazel (CoRR, 2025)

A Local Nearly Linearly Convergent First-Order Method for Nonsmooth Functions with Quadratic Growth. - Damek Davis; Liwei Jiang (Found. Comput. Math., 2025)