Foreword: special issue for the journal track of the 10th Asian Conference on Machine Learning (ACML 2018)
We welcome you to this special issue of Machine Learning Journal (MLJ) comprising of papers accepted to the journal track of the 10th Asian Conference on Machine Learning (ACML 2018) held in Beijing, China, from 14 to 16November 2018. TheACML conference is running a dedicated journal track alongside the usual conference proceedings track. We are delighted to share the contributions with you. This year's ACML journal track received 35 submissions and 7 papers have been accepted for this special issue. The reviewing process was handled by the ACML journal track committee, consisting of Wray Buntine (Monash University, Australia) Steven Hoi (Singapore Management University, Singapore) Kee Eung Kim (Korea Advanced Institute of Science and Technology, Korea) Wee Sun Lee (National University of Singapore, Singapore) Hang Li (Toutiao, China) Yu Feng Li (Nanjing University, China) and Ivor Tsang (University of Technology Sydney, Australia) with Co chairs and Editors of this special issue, Masashi Sugiyama (RIKEN/The University of Tokyo, Japan) and Yung Kyun Noh (Seoul National University, Korea) Submitted papers were assigned to committee members by the Co chairs according to the committee members' areas of expertise, while ensuring that there were no conflicts of interest. The committee members recommended reviewers and acted as metareviewers for the papers. Promising papers that did not quite meet the expected standard were allowed to be resubmitted after improvement, following the reviewing process of this journal. The paper Good Arm Identification via Bandit Feedback by Hideaki Kano, Junya Honda, Kentaro Sakamaki, Kentaro Matsuura, Atsuyoshi Nakamura, and Masashi Sugiyama introduces a new concept of selecting a good arm which is not the best but useful. The Hybrid algorithm for the Dilemma of Confidence (HDoC) for this problem is proposed with theoretical bounds on the sample complexity. For many applications that not necessarily need the best choice, the proposed algorithm in this paper can be used for choosing good enough choices after looking into a small number of samples.
Machine Learning