Damek Davis - RankMe

53 papers A* 7Journal 45Unranked 1

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	A Local Nearly Linearly Convergent First-Order Method for Nonsmooth Functions with Quadratic Growth. Found. Comput. Math. Damek Davis, Liwei Jiang
2025	J	`jnl`	Iteratively reweighted kernel machines efficiently learn sparse functions. CoRR Libin Zhu, Damek Davis, Dmitriy Drusvyatskiy, Maryam Fazel
2025	A*	`conf`	Online Covariance Estimation in Nonsmooth Stochastic Approximation. COLT Liwei Jiang, Abhishek Roy, Krishna Balasubramanian, Damek Davis, Dmitriy Drusvyatskiy, Sen Na
2025	J	`jnl`	Online Covariance Estimation in Nonsmooth Stochastic Approximation. CoRR Liwei Jiang, Abhishek Roy, Krishna Balasubramanian, Damek Davis, Dmitriy Drusvyatskiy, Sen Na
2025	J	`jnl`	Stochastic Optimization Over Proximally Smooth Sets. SIAM J. Optim. Damek Davis, Dmitriy Drusvyatskiy, Zhan Shi
2025	J	`jnl`	What is the objective of reasoning with reinforcement learning? CoRR Damek Davis, Benjamin Recht
2025	J	`jnl`	When do spectral gradient updates help in deep learning? CoRR Damek Davis, Dmitriy Drusvyatskiy
2024	J	`jnl`	A Superlinearly Convergent Subgradient Method for Sharp Semismooth Problems. Math. Oper. Res. Vasileios Charisopoulos, Damek Davis
2024	J	`jnl`	Global Optimality of the EM Algorithm for Mixtures of Two-Component Linear Regressions. IEEE Trans. Inf. Theory Jeongyeol Kwon, Wei Qian, Yudong Chen, Constantine Caramanis, Damek Davis, Nhat Ho
2024	J	`jnl`	Gradient descent with adaptive stepsize converges (nearly) linearly under fourth-order growth. CoRR Damek Davis, Dmitriy Drusvyatskiy, Liwei Jiang
2024	J	`jnl`	Stochastic algorithms with geometric step decay converge linearly on sharp functions. Math. Program. Damek Davis, Dmitriy Drusvyatskiy, Vasileios Charisopoulos
2023	A*	`conf`	Aiming towards the minimizers: fast convergence of SGD for overparametrized problems. NeurIPS Chaoyue Liu, Dmitriy Drusvyatskiy, Mikhail Belkin, Damek Davis, Yi-An Ma
2023	J	`jnl`	Aiming towards the minimizers: fast convergence of SGD for overparametrized problems. CoRR Chaoyue Liu, Dmitriy Drusvyatskiy, Mikhail Belkin, Damek Davis, Yi-An Ma
2023	J	`jnl`	Variance reduction for root-finding problems. Math. Program. Damek Davis
2022	A*	`conf`	A gradient sampling method with complexity guarantees for Lipschitz functions in high and low dimensions. NeurIPS Damek Davis, Dmitriy Drusvyatskiy, Yin Tat Lee, Swati Padmanabhan, Guanghao Ye
2022	J	`jnl`	Escaping Strict Saddle Points of the Moreau Envelope in Nonsmooth Optimization. SIAM J. Optim. Damek Davis, Mateo Díaz, Dmitriy Drusvyatskiy
2022	J	`jnl`	Graphical Convergence of Subgradients in Nonconvex Optimization and Learning. Math. Oper. Res. Damek Davis, Dmitriy Drusvyatskiy
2022	J	`jnl`	Proximal Methods Avoid Active Strict Saddles of Weakly Convex Functions. Found. Comput. Math. Damek Davis, Dmitriy Drusvyatskiy
2021	J	`jnl`	Clustering a Mixture of Gaussians with Unknown Covariance. CoRR Damek Davis, Mateo Díaz, Kaizheng Wang
2021	J	`jnl`	Escaping strict saddle points of the Moreau envelope in nonsmooth optimization. CoRR Damek Davis, Mateo Díaz, Dmitriy Drusvyatskiy
2021	J	`jnl`	From Low Probability to High Confidence in Stochastic Convex Optimization. J. Mach. Learn. Res. Damek Davis, Dmitriy Drusvyatskiy, Lin Xiao, Junyu Zhang
2021	J	`jnl`	Low-Rank Matrix Recovery with Composite Optimization: Good Conditioning and Rapid Convergence. Found. Comput. Math. Vasileios Charisopoulos, Yudong Chen, Damek Davis, Mateo Díaz, Lijun Ding, Dmitriy Drusvyatskiy
2021	J	`jnl`	Subgradient methods near active manifolds: saddle point avoidance, local convergence, and asymptotic normality. CoRR Damek Davis, Dmitriy Drusvyatskiy, Liwei Jiang
2020	A*	`conf`	High probability guarantees for stochastic convex optimization. COLT Damek Davis, Dmitriy Drusvyatskiy
2020	J	`jnl`	Stochastic Subgradient Method Converges on Tame Functions. Found. Comput. Math. Damek Davis, Dmitriy Drusvyatskiy, Sham M. Kakade, Jason D. Lee
2020	J	`jnl`	Trimmed Statistical Estimation via Variance Reduction. Math. Oper. Res. Aleksandr Y. Aravkin, Damek Davis
2019	J	`jnl`	Active strict saddles in nonsmooth optimization. CoRR Damek Davis, Dmitriy Drusvyatskiy
2019	J	`jnl`	Composite optimization for robust blind deconvolution. CoRR Vasileios Charisopoulos, Damek Davis, Mateo Díaz, Dmitriy Drusvyatskiy
2019	A*	`conf`	Global Convergence of the EM Algorithm for Mixtures of Two Component Linear Regression. COLT Jeongyeol Kwon, Wei Qian, Constantine Caramanis, Yudong Chen, Damek Davis
2019	J	`jnl`	Low-rank matrix recovery with composite optimization: good conditioning and rapid convergence. CoRR Vasileios Charisopoulos, Yudong Chen, Damek Davis, Mateo Díaz, Lijun Ding, Dmitriy Drusvyatskiy
2019	J	`jnl`	Proximally Guided Stochastic Subgradient Method for Nonsmooth, Nonconvex Problems. SIAM J. Optim. Damek Davis, Benjamin Grimmer
2019	J	`jnl`	Robust stochastic optimization with the proximal point method. CoRR Damek Davis, Dmitriy Drusvyatskiy
2019	J	`jnl`	Stochastic Model-Based Minimization of Weakly Convex Functions. SIAM J. Optim. Damek Davis, Dmitriy Drusvyatskiy
2019	J	`jnl`	Stochastic algorithms with geometric step decay converge linearly on sharp functions. CoRR Damek Davis, Dmitriy Drusvyatskiy, Vasileios Charisopoulos
2018	J	`jnl`	Forward-Backward-Half Forward Algorithm for Solving Monotone Inclusions. SIAM J. Optim. Luis M. Briceño-Arias, Damek Davis
2018	J	`jnl`	Stochastic model-based minimization of weakly convex functions. CoRR Damek Davis, Dmitriy Drusvyatskiy
2018	J	`jnl`	Stochastic model-based minimization under high-order growth. CoRR Damek Davis, Dmitriy Drusvyatskiy, Kellie J. MacPhee
2018	J	`jnl`	Stochastic subgradient method converges at the rate O(k CoRR Damek Davis, Dmitriy Drusvyatskiy
2018	J	`jnl`	Stochastic subgradient method converges on tame functions. CoRR Damek Davis, Dmitriy Drusvyatskiy, Sham M. Kakade, Jason D. Lee
2018	J	`jnl`	Subgradient Methods for Sharp Weakly Convex Functions. J. Optim. Theory Appl. Damek Davis, Dmitriy Drusvyatskiy, Kellie J. MacPhee, Courtney Paquette
2018	J	`jnl`	Uniform Graphical Convergence of Subgradients in Nonconvex Optimization and Learning. CoRR Damek Davis, Dmitriy Drusvyatskiy
2017	J	`jnl`	Beating Level-Set Methods for 5-D Seismic Data Interpolation: A Primal-Dual Alternating Approach. IEEE Trans. Computational Imaging Rajiv Kumar, Oscar López, Damek Davis, Aleksandr Y. Aravkin, Felix J. Herrmann
2017	J	`jnl`	Faster Convergence Rates of Relaxed Peaceman-Rachford and ADMM Under Regularity Assumptions. Math. Oper. Res. Damek Davis, Wotao Yin
2017	J	`jnl`	Proximally Guided Stochastic Subgradient Method for Nonsmooth, Nonconvex Problems. CoRR Damek Davis, Benjamin Grimmer
2016	J	`jnl`	A SMART Stochastic Algorithm for Nonconvex Optimization with Applications to Robust Machine Learning. CoRR Aleksandr Y. Aravkin, Damek Davis
2016	—	`conf`	The Sound of APALM Clapping: Faster Nonsmooth Nonconvex Optimization with Stochastic Asynchronous PALM. NIPS Damek Davis, Brent Edmunds, Madeleine Udell
2015	J	`jnl`	An O(nlog(n)) Algorithm for Projecting Onto the Ordered Weighted ℓ CoRR Damek Davis
2015	J	`jnl`	Convergence Rate Analysis of Primal-Dual Splitting Schemes. SIAM J. Optim. Damek Davis
2015	J	`jnl`	Convergence Rate Analysis of the Forward-Douglas-Rachford Splitting Scheme. SIAM J. Optim. Damek Davis
2015	A*	`conf`	Multi-view feature engineering and learning. CVPR Jingming Dong, Nikolaos Karianakis, Damek Davis, Joshua Hernandez, Jonathan Balzer, Stefano Soatto
2014	A*	`conf`	Asymmetric Sparse Kernel Approximations for Large-Scale Visual Search. CVPR Damek Davis, Jonathan Balzer, Stefano Soatto
2014	J	`jnl`	Tactical Scheduling for Precision Air Traffic Operations: Past Research and Current Problems. J. Aerosp. Inf. Syst. Douglas R. Isaacson, Alexander V. Sadovsky, Damek Davis
2013	J	`jnl`	On the Design and Analysis of Multiple View Descriptors. CoRR Jingming Dong, Jonathan Balzer, Damek Davis, Joshua Hernandez, Stefano Soatto