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. 2012.13115
  4. Cited By
Upper Confidence Bounds for Combining Stochastic Bandits

Upper Confidence Bounds for Combining Stochastic Bandits

24 December 2020
Ashok Cutkosky
Abhimanyu Das
Manish Purohit
ArXiv (abs)PDFHTML

Papers citing "Upper Confidence Bounds for Combining Stochastic Bandits"

6 / 6 papers shown
Title
Offline-to-online hyperparameter transfer for stochastic bandits
Dravyansh Sharma
Arun Sai Suggala
OffRL
103
4
0
06 Jan 2025
Causal Bandits: The Pareto Optimal Frontier of Adaptivity, a Reduction
  to Linear Bandits, and Limitations around Unknown Marginals
Causal Bandits: The Pareto Optimal Frontier of Adaptivity, a Reduction to Linear Bandits, and Limitations around Unknown Marginals
Ziyi Liu
Idan Attias
Daniel M. Roy
CML
51
1
0
01 Jul 2024
Linear Bandits with Memory: from Rotting to Rising
Linear Bandits with Memory: from Rotting to Rising
Giulia Clerici
Pierre Laforgue
Nicolò Cesa-Bianchi
50
3
0
16 Feb 2023
Universal and data-adaptive algorithms for model selection in linear
  contextual bandits
Universal and data-adaptive algorithms for model selection in linear contextual bandits
Vidya Muthukumar
A. Krishnamurthy
71
5
0
08 Nov 2021
The Pareto Frontier of model selection for general Contextual Bandits
The Pareto Frontier of model selection for general Contextual Bandits
T. V. Marinov
Julian Zimmert
98
22
0
25 Oct 2021
Pareto Optimal Model Selection in Linear Bandits
Pareto Optimal Model Selection in Linear Bandits
Yinglun Zhu
Robert D. Nowak
43
14
0
12 Feb 2021
1