6 citations · 7 across the 4 of their papers we have counts for
6 papers
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…
Kernel-convoluted Deep Neural Networks with Data Augmentation
Minjin Kim, Young-geun Kim, Dongha Kim +2
The Mixup method (Zhang et al. 2018), which uses linearly interpolated data, has emerged as an effective data augmentation tool to improve generalization performance and the robust…
Principled learning method for Wasserstein distributionally robust optimization with local perturbations
Yongchan Kwon, Wonyoung Kim, Joong-Ho Won +1
Wasserstein distributionally robust optimization (WDRO) attempts to learn a model that minimizes the local worst-case risk in the vicinity of the empirical data distribution define…
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,…
Principled analytic classifier for positive-unlabeled learning via weighted integral probability metric
Yongchan Kwon, Wonyoung Kim, Masashi Sugiyama +1
We consider the problem of learning a binary classifier from only positive and unlabeled observations (called PU learning). Recent studies in PU learning have shown superior perfor…