ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2006.03912
  4. Cited By
Unconstrained Online Optimization: Dynamic Regret Analysis of Strongly
  Convex and Smooth Problems
v1v2 (latest)

Unconstrained Online Optimization: Dynamic Regret Analysis of Strongly Convex and Smooth Problems

6 June 2020
Ting-Jui Chang
Shahin Shahrampour
    ODL
ArXiv (abs)PDFHTML

Papers citing "Unconstrained Online Optimization: Dynamic Regret Analysis of Strongly Convex and Smooth Problems"

2 / 2 papers shown
Title
Efficient Online Learning with Memory via Frank-Wolfe Optimization:
  Algorithms with Bounded Dynamic Regret and Applications to Control
Efficient Online Learning with Memory via Frank-Wolfe Optimization: Algorithms with Bounded Dynamic Regret and Applications to Control
Hongyu Zhou
Zirui Xu
Vasileios Tzoumas
140
13
0
02 Jan 2023
Improving Dynamic Regret in Distributed Online Mirror Descent Using
  Primal and Dual Information
Improving Dynamic Regret in Distributed Online Mirror Descent Using Primal and Dual Information
Nima Eshraghi
Ben Liang
90
9
0
07 Dec 2021
1