3 papers
cs.LG2026
Adversarial Bandit Optimization with Globally Bounded Perturbations to Linear Losses
Zhuoyu Cheng, Kohei Hatano, Eiji Takimoto
We study a class of adversarial bandit optimization problems in which the loss functions may be non-convex and non-smooth. In each round, the learner observes a loss that consists…
cs.LG2025
Adversarial bandit optimization for approximately linear functions
Zhuoyu Cheng, Kohei Hatano, Eiji Takimoto
We consider a bandit optimization problem for nonconvex and non-smooth functions, where in each trial the loss function is the sum of a linear function and a small but arbitrary pe…
cs.LG2025
Multi-thresholding Good Arm Identification with Bandit Feedback
Xuanke Jiang, Sherief Hashima, Kohei Hatano +1
We consider a good arm identification problem in a stochastic bandit setting with multi-objectives, where each arm is associated with a distribution defined over…