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. 2110.07807
  4. Cited By
Provable Regret Bounds for Deep Online Learning and Control

Provable Regret Bounds for Deep Online Learning and Control

15 October 2021
Xinyi Chen
Edgar Minasyan
Jason D. Lee
Elad Hazan
ArXivPDFHTML

Papers citing "Provable Regret Bounds for Deep Online Learning and Control"

6 / 6 papers shown
Title
Online Nonstochastic Model-Free Reinforcement Learning
Online Nonstochastic Model-Free Reinforcement Learning
Udaya Ghai
Arushi Gupta
Wenhan Xia
Karan Singh
Elad Hazan
OffRL
31
6
0
27 May 2023
Neural Exploitation and Exploration of Contextual Bandits
Neural Exploitation and Exploration of Contextual Bandits
Yikun Ban
Yuchen Yan
A. Banerjee
Jingrui He
36
8
0
05 May 2023
Online Nonstochastic Control with Adversarial and Static Constraints
Online Nonstochastic Control with Adversarial and Static Constraints
Xin Liu
Zixi Yang
Lei Ying
30
5
0
05 Feb 2023
A Review of Machine Learning Methods Applied to Structural Dynamics and
  Vibroacoustic
A Review of Machine Learning Methods Applied to Structural Dynamics and Vibroacoustic
Barbara Z Cunha
C. Droz
A. Zine
Stéphane Foulard
M. Ichchou
AI4CE
29
84
0
13 Apr 2022
A Regret Minimization Approach to Multi-Agent Control
A Regret Minimization Approach to Multi-Agent Control
Udaya Ghai
Udari Madhushani
Naomi Ehrich Leonard
Elad Hazan
35
5
0
28 Jan 2022
Boosting Simple Learners
Boosting Simple Learners
N. Alon
Alon Gonen
Elad Hazan
Shay Moran
41
19
0
31 Jan 2020
1