59 citations · 62 across the 7 of their papers we have counts for
7 papers
Thompson Exploration with Best Challenger Rule in Best Arm Identification
Jongyeong Lee, Junya Honda, Masashi Sugiyama
This paper studies the fixed-confidence best arm identification (BAI) problem in the bandit framework in the canonical single-parameter exponential models. For this problem, many p…
Optimality of Thompson Sampling with Noninformative Priors for Pareto Bandits
Jongyeong Lee, Junya Honda, Chao-Kai Chiang +1
In the stochastic multi-armed bandit problem, a randomized probability matching policy called Thompson sampling (TS) has shown excellent performance in various reward models. In ad…
The Choice of Noninformative Priors for Thompson Sampling in Multiparameter Bandit Models
Jongyeong Lee, Chao-Kai Chiang, Masashi Sugiyama
Thompson sampling (TS) has been known for its outstanding empirical performance supported by theoretical guarantees across various reward models in the classical stochastic multi-a…
A Symmetric Loss Perspective of Reliable Machine Learning
Nontawat Charoenphakdee, Jongyeong Lee, Masashi Sugiyama
When minimizing the empirical risk in binary classification, it is a common practice to replace the zero-one loss with a surrogate loss to make the learning objective feasible to o…
Learning Only from Relevant Keywords and Unlabeled Documents
Nontawat Charoenphakdee, Jongyeong Lee, Yiping Jin +2
We consider a document classification problem where document labels are absent but only relevant keywords of a target class and unlabeled documents are given. Although heuristic me…
Domain Discrepancy Measure for Complex Models in Unsupervised Domain Adaptation
Jongyeong Lee, Nontawat Charoenphakdee, Seiichi Kuroki +1
Appropriately evaluating the discrepancy between domains is essential for the success of unsupervised domain adaptation. In this paper, we first point out that existing discrepancy…
On Symmetric Losses for Learning from Corrupted Labels
Nontawat Charoenphakdee, Jongyeong Lee, Masashi Sugiyama
This paper aims to provide a better understanding of a symmetric loss. First, we emphasize that using a symmetric loss is advantageous in the balanced error rate (BER) minimization…