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Adaptive Discretization against an Adversary: Lipschitz bandits, Dynamic Pricing, and Auction Tuning
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Adaptive Discretization against an Adversary: Lipschitz bandits, Dynamic Pricing, and Auction Tuning

22 June 2020
Chara Podimata
Aleksandrs Slivkins
ArXiv (abs)PDFHTML

Papers citing "Adaptive Discretization against an Adversary: Lipschitz bandits, Dynamic Pricing, and Auction Tuning"

4 / 4 papers shown
Adversarial Bandit over Bandits: Hierarchical Bandits for Online Configuration Management
Adversarial Bandit over Bandits: Hierarchical Bandits for Online Configuration Management
C. Avin
Zvi Lotker
Shie Mannor
G. Shabat
H. Shteingart
Roey Yadgar
375
0
0
25 May 2025
Batched Stochastic Bandit for Nondegenerate Functions
Batched Stochastic Bandit for Nondegenerate FunctionsIEEE Transactions on Information Theory (IEEE Trans. Inf. Theory), 2024
Yu Liu
Yunlu Shu
Tianyu Wang
621
2
0
09 May 2024
Regret Minimization with Performative Feedback
Regret Minimization with Performative FeedbackInternational Conference on Machine Learning (ICML), 2022
Meena Jagadeesan
Tijana Zrnic
Celestine Mendler-Dünner
308
41
0
01 Feb 2022
Contextual Search in the Presence of Adversarial Corruptions
Contextual Search in the Presence of Adversarial CorruptionsOperational Research (OR), 2020
A. Krishnamurthy
Thodoris Lykouris
Chara Podimata
Robert Schapire
477
6
0
26 Feb 2020
1
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