23 citations · 33 across the 8 of their papers we have counts for
9 papers
Experimental Design for Semiparametric Bandits
Seok-Jin Kim, Gi-Soo Kim, Min-hwan Oh
We study finite-armed semiparametric bandits, where each arm's reward combines a linear component with an unknown, potentially adversarial shift. This model strictly generalizes cl…
Pursuing Overall Welfare in Federated Learning through Sequential Decision Making
Seok-Ju Hahn, Gi-Soo Kim, Junghye Lee
In traditional federated learning, a single global model cannot perform equally well for all clients. Therefore, the need to achieve the client-level fairness in federated system h…
Bandit-supported care planning for older people with complex health and care needs
Gi-Soo Kim, Young Suh Hong, Tae Hoon Lee +2
Long-term care service for old people is in great demand in most of the aging societies. The number of nursing homes residents is increasing while the number of care providers is l…
GBOSE: Generalized Bandit Orthogonalized Semiparametric Estimation
Mubarrat Chowdhury, Elkhan Ismayilzada, Khalequzzaman Sayem +1
In sequential decision-making scenarios i.e., mobile health recommendation systems revenue management contextual multi-armed bandit algorithms have garnered attention for their per…
Robust Tests in Online Decision-Making
Gi-Soo Kim, Hyun-Joon Yang, Jane P. Kim
Bandit algorithms are widely used in sequential decision problems to maximize the cumulative reward. One potential application is mobile health, where the goal is to promote the us…
Semi-Parametric Contextual Bandits with Graph-Laplacian Regularization
Young-Geun Choi, Gi-Soo Kim, Seunghoon Paik +1
Non-stationarity is ubiquitous in human behavior and addressing it in the contextual bandits is challenging. Several works have addressed the problem by investigating semi-parametr…
Doubly robust Thompson sampling for linear payoffs
Wonyoung Kim, Gi-soo Kim, Myunghee Cho Paik
A challenging aspect of the bandit problem is that a stochastic reward is observed only for the chosen arm and the rewards of other arms remain missing. The dependence of the arm c…
Doubly-Robust Lasso Bandit
Gi-Soo Kim, Myunghee Cho Paik
Contextual multi-armed bandit algorithms are widely used in sequential decision tasks such as news article recommendation systems, web page ad placement algorithms, and mobile heal…
Contextual Multi-armed Bandit Algorithm for Semiparametric Reward Model
Gi-Soo Kim, Myunghee Cho Paik
Contextual multi-armed bandit (MAB) algorithms have been shown promising for maximizing cumulative rewards in sequential decision tasks such as news article recommendation systems,…