6 papers
I.i.d. Prophet Inequalities with Discounted Rewards: As Hard as the Non-i.i.d. Case
Jung-hun Kim, Vianney Perchet
We study prophet inequalities with discounted rewards, where i.i.d. base rewards are multiplicatively discounted over time. Our main message is that even this structured and arbitr…
Asymptotically Optimal Learning for Parametric Prophet Inequalities
Jung-hun Kim, Anna Grebennikova, Vianney Perchet
We study learning in prophet inequalities with i.i.d. rewards drawn from an exponential-type parametric family with an unknown parameter , a class that includes exponential, Pa…
Learning in Prophet Inequalities with Noisy Observations
Jung-hun Kim, Vianney Perchet
We study the prophet inequality, a fundamental problem in online decision-making and optimal stopping, in a practical setting where rewards are observed only through noisy realizat…
Adversarial Bandits against Arbitrary Strategies
Jung-hun Kim, Se-Young Yun
We study the adversarial bandit problem against arbitrary strategies, where the difficulty is captured by an unknown parameter , which is the number of switches in the best arm…
An Adaptive Approach for Infinitely Many-armed Bandits under Generalized Rotting Constraints
Jung-hun Kim, Milan Vojnovic, Se-Young Yun
In this study, we consider the infinitely many-armed bandit problems in a rested rotting setting, where the mean reward of an arm may decrease with each pull, while otherwise, it r…
Contextual Linear Bandits under Noisy Features: Towards Bayesian Oracles
Jung-hun Kim, Se-Young Yun, Minchan Jeong +3
We study contextual linear bandit problems under feature uncertainty, where the features are noisy and have missing entries. To address the challenges posed by this noise, we analy…