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A Richer Theory of Convex Constrained Optimization with Reduced
  Projections and Improved Rates
v1v2 (latest)

A Richer Theory of Convex Constrained Optimization with Reduced Projections and Improved Rates

11 August 2016
Tianbao Yang
Qihang Lin
Lijun Zhang
ArXiv (abs)PDFHTML

Papers citing "A Richer Theory of Convex Constrained Optimization with Reduced Projections and Improved Rates"

6 / 6 papers shown
Federated Composite Optimization
Federated Composite OptimizationInternational Conference on Machine Learning (ICML), 2020
Honglin Yuan
Manzil Zaheer
Sashank J. Reddi
FedML
352
67
0
17 Nov 2020
Projection-free Online Learning over Strongly Convex Sets
Projection-free Online Learning over Strongly Convex SetsAAAI Conference on Artificial Intelligence (AAAI), 2020
Yuanyu Wan
Lijun Zhang
328
29
0
16 Oct 2020
Projection Efficient Subgradient Method and Optimal Nonsmooth
  Frank-Wolfe Method
Projection Efficient Subgradient Method and Optimal Nonsmooth Frank-Wolfe MethodNeural Information Processing Systems (NeurIPS), 2020
K. K. Thekumparampil
Prateek Jain
Praneeth Netrapalli
Sewoong Oh
223
26
0
05 Oct 2020
Gradient-based Sparse Principal Component Analysis with Extensions to
  Online Learning
Gradient-based Sparse Principal Component Analysis with Extensions to Online Learning
Yixuan Qiu
Jing Lei
Kathryn Roeder
206
15
0
19 Nov 2019
Optimization with Non-Differentiable Constraints with Applications to
  Fairness, Recall, Churn, and Other Goals
Optimization with Non-Differentiable Constraints with Applications to Fairness, Recall, Churn, and Other Goals
Andrew Cotter
Heinrich Jiang
S. Wang
Taman Narayan
Maya R. Gupta
Seungil You
Karthik Sridharan
313
177
0
11 Sep 2018
RSG: Beating Subgradient Method without Smoothness and Strong Convexity
RSG: Beating Subgradient Method without Smoothness and Strong Convexity
Tianbao Yang
Qihang Lin
996
90
0
09 Dec 2015
1
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