10 papers · 1 filter
Adversarial Bandit Optimization with Globally Bounded Perturbations to Convex Losses
Zhuoyu Cheng, Kohei Hatano, Eiji Takimoto
We study adversarial bandit optimization in which the loss functions may be non-convex and non-smooth. In each round, the learner selects an action and observes only the loss incur…
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…
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…
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…
Pure exploration in multi-armed bandits with low rank structure using oblivious sampler
Yaxiong Liu, Atsuyoshi Nakamura, Kohei Hatano +1
In this paper, we consider the low rank structure of the reward sequence of the pure exploration problems. Firstly, we propose the separated setting in pure exploration problem, wh…
Boosting-based Construction of BDDs for Linear Threshold Functions and Its Application to Verification of Neural Networks
Yiping Tang, Kohei Hatano, Eiji Takimoto
Understanding the characteristics of neural networks is important but difficult due to their complex structures and behaviors. Some previous work proposes to transform neural netwo…