23 citations · 44 across the 13 of their papers we have counts for
13 papers
Doubly-Robust Off-Policy Evaluation with Estimated Logging Policy
Kyungbok Lee, Myunghee Cho Paik
We introduce a novel doubly-robust (DR) off-policy evaluation (OPE) estimator for Markov decision processes, DRUnknown, designed for situations where both the logging policy and th…
Wasserstein Geodesic Generator for Conditional Distributions
Young-geun Kim, Kyungbok Lee, Youngwon Choi +2
Generating samples given a specific label requires estimating conditional distributions. We derive a tractable upper bound of the Wasserstein distance between conditional distribut…
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…
Double Doubly Robust Thompson Sampling for Generalized Linear Contextual Bandits
Wonyoung Kim, Kyungbok Lee, Myunghee Cho Paik
We propose a novel contextual bandit algorithm for generalized linear rewards with an regret over rounds where is the minimum eigenvalue of th…
Squeeze All: Novel Estimator and Self-Normalized Bound for Linear Contextual Bandits
Wonyoung Kim, Myunghee Cho Paik, Min-hwan Oh
We propose a linear contextual bandit algorithm with regret bound, where is the dimension of contexts and isthe time horizon. Our proposed algorithm is…
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…
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…
A general class of quasi-independence tests for left-truncated right-censored data
Young-Geun Choi, Wei-Yann Tsai, Myunghee Cho Paik
In survival studies, classical inferences for left-truncated data require quasi-independence, a property that the joint density of truncation time and failure time is factorizable…