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. 2405.12439
  4. Cited By
No-Regret M${}^{\natural}$-Concave Function Maximization: Stochastic
  Bandit Algorithms and NP-Hardness of Adversarial Full-Information Setting

No-Regret M♮{}^{\natural}♮-Concave Function Maximization: Stochastic Bandit Algorithms and NP-Hardness of Adversarial Full-Information Setting

21 May 2024
Taihei Oki
Shinsaku Sakaue
ArXivPDFHTML

Papers citing "No-Regret M${}^{\natural}$-Concave Function Maximization: Stochastic Bandit Algorithms and NP-Hardness of Adversarial Full-Information Setting"

2 / 2 papers shown
Title
Sum-max Submodular Bandits
Sum-max Submodular Bandits
Stephen Pasteris
Alberto Rumi
Fabio Vitale
Nicolò Cesa-Bianchi
18
2
0
10 Nov 2023
Adversarial Combinatorial Bandits with General Non-linear Reward
  Functions
Adversarial Combinatorial Bandits with General Non-linear Reward Functions
Xi Chen
Yanjun Han
Yining Wang
30
16
0
05 Jan 2021
1