Showing cs.LGShow all
3 papers · 1 filter
cs.LG2025
Functional multi-armed bandit and the best function identification problems
Yuriy Dorn, Aleksandr Katrutsa, Ilgam Latypov +1
Bandit optimization usually refers to the class of online optimization problems with limited feedback, namely, a decision maker uses only the objective value at the current point t…
cs.LG2025
Optimizing Online Advertising with Multi-Armed Bandits: Mitigating the Cold Start Problem under Auction Dynamics
Anastasiia Soboleva, Andrey Pudovikov, Roman Snetkov +3
Online advertising platforms often face a common challenge: the cold start problem. Insufficient behavioral data (clicks) makes accurate click-through rate (CTR) forecasting of new…
cs.LG2024
Fast UCB-type algorithms for stochastic bandits with heavy and super heavy symmetric noise
Yuriy Dorn, Aleksandr Katrutsa, Ilgam Latypov +1
In this study, we propose a new method for constructing UCB-type algorithms for stochastic multi-armed bandits based on general convex optimization methods with an inexact oracle.…