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. 2202.04786
  4. Cited By
No-Regret Learning in Dynamic Stackelberg Games

No-Regret Learning in Dynamic Stackelberg Games

10 February 2022
Niklas Lauffer
Mahsa Ghasemi
Abolfazl Hashemi
Y. Savas
Ufuk Topcu
ArXiv (abs)PDFHTML

Papers citing "No-Regret Learning in Dynamic Stackelberg Games"

4 / 4 papers shown
Title
Distributionally Safe Reinforcement Learning under Model Uncertainty: A
  Single-Level Approach by Differentiable Convex Programming
Distributionally Safe Reinforcement Learning under Model Uncertainty: A Single-Level Approach by Differentiable Convex Programming
A. Chriat
Chuangchuang Sun
90
1
0
03 Oct 2023
Decentralized Online Bandit Optimization on Directed Graphs with Regret
  Bounds
Decentralized Online Bandit Optimization on Directed Graphs with Regret Bounds
Johan Ostman
Ather Gattami
D. Gillblad
79
1
0
27 Jan 2023
Online Learning in Stackelberg Games with an Omniscient Follower
Online Learning in Stackelberg Games with an Omniscient Follower
Geng Zhao
Banghua Zhu
Jiantao Jiao
Michael I. Jordan
423
15
0
27 Jan 2023
The Sample Complexity of Online Contract Design
The Sample Complexity of Online Contract Design
Banghua Zhu
Stephen Bates
Zhuoran Yang
Yixin Wang
Jiantao Jiao
Michael I. Jordan
125
51
0
10 Nov 2022
1