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Damek Davis
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- affiliation: University of California, Los Angeles, USA
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2020 – today
- 2024
- [j21]Vasileios Charisopoulos
, Damek Davis
A Superlinearly Convergent Subgradient Method for Sharp Semismooth Problems. Math. Oper. Res. 49(3): 1678-1709 (2024) - [j20]Damek Davis, Dmitriy Drusvyatskiy
Stochastic algorithms with geometric step decay converge linearly on sharp functions. Math. Program. 207(1): 145-190 (2024) - [j19]Jeongyeol Kwon
Global Optimality of the EM Algorithm for Mixtures of Two-Component Linear Regressions. IEEE Trans. Inf. Theory 70(9): 6519-6546 (2024) - [i19]Damek Davis, Dmitriy Drusvyatskiy, Liwei Jiang:
Gradient descent with adaptive stepsize converges (nearly) linearly under fourth-order growth. CoRR abs/2409.19791 (2024) - 2023
- [j18]Damek Davis
Variance reduction for root-finding problems. Math. Program. 197(1): 375-410 (2023) - [j17]Damek Shea Davis, Oktay Günlük, Volker Kaibel, Giacomo Nannicini, Xa-Xiang Yuan:
Special Issue: International Symposium on Mathematical Programming 2022. Math. Program. 200(2): 629-631 (2023) - [c7]Chaoyue Liu, Dmitriy Drusvyatskiy, Misha Belkin, Damek Davis, Yi-An Ma:
Aiming towards the minimizers: fast convergence of SGD for overparametrized problems. NeurIPS 2023 - [i18]Chaoyue Liu, Dmitriy Drusvyatskiy, Mikhail Belkin, Damek Davis, Yi-An Ma:
Aiming towards the minimizers: fast convergence of SGD for overparametrized problems. CoRR abs/2306.02601 (2023) - 2022
- [j16]Damek Davis, Dmitriy Drusvyatskiy:
Proximal Methods Avoid Active Strict Saddles of Weakly Convex Functions. Found. Comput. Math. 22(2): 561-606 (2022) - [j15]Damek Davis
Graphical Convergence of Subgradients in Nonconvex Optimization and Learning. Math. Oper. Res. 47(1): 209-231 (2022) - [j14]Damek Davis, Mateo Díaz, Dmitriy Drusvyatskiy
Escaping Strict Saddle Points of the Moreau Envelope in Nonsmooth Optimization. SIAM J. Optim. 32(3): 1958-1983 (2022) - [c6]Damek Davis, Dmitriy Drusvyatskiy, Yin Tat Lee, Swati Padmanabhan, Guanghao Ye:
A gradient sampling method with complexity guarantees for Lipschitz functions in high and low dimensions. NeurIPS 2022 - 2021
- [j13]Vasileios Charisopoulos
Low-Rank Matrix Recovery with Composite Optimization: Good Conditioning and Rapid Convergence. Found. Comput. Math. 21(6): 1505-1593 (2021) - [j12]Damek Davis, Dmitriy Drusvyatskiy, Lin Xiao, Junyu Zhang:
From Low Probability to High Confidence in Stochastic Convex Optimization. J. Mach. Learn. Res. 22: 49:1-49:38 (2021) - [i17]Damek Davis, Mateo Díaz, Dmitriy Drusvyatskiy:
Escaping strict saddle points of the Moreau envelope in nonsmooth optimization. CoRR abs/2106.09815 (2021) - [i16]Damek Davis, Dmitriy Drusvyatskiy, Liwei Jiang:
Subgradient methods near active manifolds: saddle point avoidance, local convergence, and asymptotic normality. CoRR abs/2108.11832 (2021) - [i15]Damek Davis, Mateo Díaz, Kaizheng Wang:
Clustering a Mixture of Gaussians with Unknown Covariance. CoRR abs/2110.01602 (2021) - 2020
- [j11]Damek Davis, Dmitriy Drusvyatskiy, Sham M. Kakade, Jason D. Lee:
Stochastic Subgradient Method Converges on Tame Functions. Found. Comput. Math. 20(1): 119-154 (2020) - [j10]Aleksandr Y. Aravkin, Damek Davis
Trimmed Statistical Estimation via Variance Reduction. Math. Oper. Res. 45(1): 292-322 (2020) - [c5]Damek Davis, Dmitriy Drusvyatskiy:
High probability guarantees for stochastic convex optimization. COLT 2020: 1411-1427
2010 – 2019
- 2019
- [j9]Damek Davis, Dmitriy Drusvyatskiy
Stochastic Model-Based Minimization of Weakly Convex Functions. SIAM J. Optim. 29(1): 207-239 (2019) - [j8]Damek Davis, Benjamin Grimmer
Proximally Guided Stochastic Subgradient Method for Nonsmooth, Nonconvex Problems. SIAM J. Optim. 29(3): 1908-1930 (2019) - [c4]Jeongyeol Kwon, Wei Qian, Constantine Caramanis, Yudong Chen, Damek Davis:
Global Convergence of the EM Algorithm for Mixtures of Two Component Linear Regression. COLT 2019: 2055-2110 - [i14]Vasileios Charisopoulos, Damek Davis, Mateo Díaz, Dmitriy Drusvyatskiy:
Composite optimization for robust blind deconvolution. CoRR abs/1901.01624 (2019) - [i13]Vasileios Charisopoulos, Yudong Chen, Damek Davis, Mateo Díaz, Lijun Ding, Dmitriy Drusvyatskiy:
Low-rank matrix recovery with composite optimization: good conditioning and rapid convergence. CoRR abs/1904.10020 (2019) - [i12]Damek Davis, Dmitriy Drusvyatskiy, Vasileios Charisopoulos:
Stochastic algorithms with geometric step decay converge linearly on sharp functions. CoRR abs/1907.09547 (2019) - [i11]Damek Davis, Dmitriy Drusvyatskiy:
Robust stochastic optimization with the proximal point method. CoRR abs/1907.13307 (2019) - [i10]Damek Davis, Dmitriy Drusvyatskiy:
Active strict saddles in nonsmooth optimization. CoRR abs/1912.07146 (2019) - 2018
- [j7]Damek Davis, Dmitriy Drusvyatskiy
Subgradient Methods for Sharp Weakly Convex Functions. J. Optim. Theory Appl. 179(3): 962-982 (2018) - [j6]Luis M. Briceño-Arias
Forward-Backward-Half Forward Algorithm for Solving Monotone Inclusions. SIAM J. Optim. 28(4): 2839-2871 (2018) - [i9]Damek Davis, Dmitriy Drusvyatskiy:
Stochastic subgradient method converges at the rate O(k-1/4) on weakly convex functions. CoRR abs/1802.02988 (2018) - [i8]Damek Davis, Dmitriy Drusvyatskiy:
Stochastic model-based minimization of weakly convex functions. CoRR abs/1803.06523 (2018) - [i7]Damek Davis, Dmitriy Drusvyatskiy, Sham M. Kakade, Jason D. Lee:
Stochastic subgradient method converges on tame functions. CoRR abs/1804.07795 (2018) - [i6]Damek Davis, Dmitriy Drusvyatskiy, Kellie J. MacPhee:
Stochastic model-based minimization under high-order growth. CoRR abs/1807.00255 (2018) - [i5]Damek Davis, Dmitriy Drusvyatskiy:
Uniform Graphical Convergence of Subgradients in Nonconvex Optimization and Learning. CoRR abs/1810.07590 (2018) - 2017
- [j5]Damek Davis, Wotao Yin:
Faster Convergence Rates of Relaxed Peaceman-Rachford and ADMM Under Regularity Assumptions. Math. Oper. Res. 42(3): 783-805 (2017) - [j4]Rajiv Kumar, Oscar López
Beating Level-Set Methods for 5-D Seismic Data Interpolation: A Primal-Dual Alternating Approach. IEEE Trans. Computational Imaging 3(2): 264-274 (2017) - [i4]Damek Davis, Benjamin Grimmer:
Proximally Guided Stochastic Subgradient Method for Nonsmooth, Nonconvex Problems. CoRR abs/1707.03505 (2017) - 2016
- [c3]Damek Davis, Brent Edmunds, Madeleine Udell:
The Sound of APALM Clapping: Faster Nonsmooth Nonconvex Optimization with Stochastic Asynchronous PALM. NIPS 2016: 226-234 - [i3]Aleksandr Y. Aravkin, Damek Davis:
A SMART Stochastic Algorithm for Nonconvex Optimization with Applications to Robust Machine Learning. CoRR abs/1610.01101 (2016) - 2015
- [j3]Damek Davis:
Convergence Rate Analysis of the Forward-Douglas-Rachford Splitting Scheme. SIAM J. Optim. 25(3): 1760-1786 (2015) - [j2]Damek Davis:
Convergence Rate Analysis of Primal-Dual Splitting Schemes. SIAM J. Optim. 25(3): 1912-1943 (2015) - [c2]Jingming Dong, Nikolaos Karianakis, Damek Davis, Joshua Hernandez, Jonathan Balzer, Stefano Soatto:
Multi-view feature engineering and learning. CVPR 2015: 3251-3260 - [i2]Damek Davis:
An O(nlog(n)) Algorithm for Projecting Onto the Ordered Weighted ℓ1 Norm Ball. CoRR abs/1505.00870 (2015) - 2014
- [j1]Douglas R. Isaacson, Alexander V. Sadovsky, Damek Davis:
Tactical Scheduling for Precision Air Traffic Operations: Past Research and Current Problems. J. Aerosp. Inf. Syst. 11(4): 234-257 (2014) - [c1]Damek Davis, Jonathan Balzer, Stefano Soatto:
Asymmetric Sparse Kernel Approximations for Large-Scale Visual Search. CVPR 2014: 2115-2122 - 2013
- [i1]Jingming Dong, Jonathan Balzer, Damek Davis, Joshua Hernandez, Stefano Soatto:
On the Design and Analysis of Multiple View Descriptors. CoRR abs/1311.6048 (2013)
Coauthor Index
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