2 citations · 3 across the 15 of their papers we have counts for
6 papers · 1 filter
Optimal Regret of Bernoulli Bandits under Global Differential Privacy
Achraf Azize, Yulian Wu, Junya Honda +3
As sequential learning algorithms are increasingly applied to real life, ensuring data privacy while maintaining their utilities emerges as a timely question. In this context, regr…
Follow-the-Perturbed-Leader with Fréchet-type Tail Distributions: Optimality in Adversarial Bandits and Best-of-Both-Worlds
Jongyeong Lee, Junya Honda, Shinji Ito +1
This paper studies the optimality of the Follow-the-Perturbed-Leader (FTPL) policy in both adversarial and stochastic -armed bandits. Despite the widespread use of the Follow-th…
On Optimal Robustness to Adversarial Corruption in Online Decision Problems
Shinji Ito
This paper considers two fundamental sequential decision-making problems: the problem of prediction with expert advice and the multi-armed bandit problem. We focus on stochastic re…
Near-Optimal Regret Bounds for Contextual Combinatorial Semi-Bandits with Linear Payoff Functions
Kei Takemura, Shinji Ito, Daisuke Hatano +4
The contextual combinatorial semi-bandit problem with linear payoff functions is a decision-making problem in which a learner chooses a set of arms with the feature vectors in each…
An Arm-Wise Randomization Approach to Combinatorial Linear Semi-Bandits
Kei Takemura, Shinji Ito
Combinatorial linear semi-bandits (CLS) are widely applicable frameworks of sequential decision-making, in which a learner chooses a subset of arms from a given set of arms associa…
Causal Bandits with Propagating Inference
Akihiro Yabe, Daisuke Hatano, Hanna Sumita +4
Bandit is a framework for designing sequential experiments. In each experiment, a learner selects an arm and obtains an observation corresponding to . Theore…