Foreword: special issue for the journal track of the 12th Asian conference on machine learning (ACML 2020)
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Foreword: special issue for the journal track of the 12th Asian conference on machine learning (ACML 2020) Kee‑Eung Kim1 · Vineeth N. Balasubramanian2
© The Author(s), under exclusive licence to Springer Science+Business Media LLC, part of Springer Nature 2020
We welcome you to this special issue of Machine Learning Journal (MLJ), comprising of papers accepted to the journal track of the 12th Asian Conference on Machine Learning (ACML 2020), held virtually from 18 to 20 November 2020 (https://www.acml-conf. org/2020/). The ACML conference runs 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 a total of 38 submissions and 6 papers have been accepted for this special issue, after two rigorous rounds of reviews. Promising papers that did not quite meet the expected standard were allowed to be resubmitted after improvement, following the reviewing policy of this journal. The program committee members of ACML made bids on the papers for review assignment while ensuring that there were no conflicts of interest. The senior program committee members of ACML also followed the same process, acting as meta-reviewers for the papers. The senior program committee members who contributed to the reviewing process are: Aditya Menon (Google, USA). Alice Oh (KAIST, Korea). Dinh Phung (Monash University, Australia). Gang Niu (RIKEN AIP, Japan). Jaegul Choo (KAIST, Korea). James Tin-Yau Kwok (The Hong Kong University of Science and Technology). Jian Li (Tsinghua University, China). Junmo Kim (KAIST, Korea). Kohei Hatano (Kyushu University, Japan). Kun Zhang (Carnegie Mellon University, USA). Li Cheng (University of Alberta, Canada). Mehmet Gönen (Koç University, Turkey). Minh Ha Quang (RIKEN AIP, Japan). P. K. Srijith (IIT Hyderabad, India). Qibin Zhao (RIKEN AIP, Japan). Seungjin Choi (BARO AI, Korea). * Kee‑Eung Kim [email protected] 1
KAIST, Daejeon, Korea
2
Indian Institute of Technology, Hyderabad, India
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Shinichi Nakajima (Technische Universität Berlin, Germany). Steven Hoi (Singapore Management University, Singapore). Sung Ju Hwang (KAIST, Korea). Takafumi Kanamori (Tokyo Institute of Technology/RIKEN AIP, Japan). Takayuki Okatani (Tohoku University/RIKEN AIP, Japan). Takayuki Osogami (IBM Research—Tokyo, Japan). Tao Qin (Microsoft Research Asia, China). Vincent Zheng (WeBank, China). Wei Lu (Singapore University of Technology and Design, Singapore). Wittawat Jitkrittum (Google Research, Germany). Yang Yu (Nanjing University, China). Yasuo Tabei (RIKEN AIP, Japan). Yung-Kyun Noh (Seoul National University, Korea). Zhouchen Lin (Peking University, China). The paper ’Learning with Mitigating Random Consistency from the Accuracy Measure’ by Yuhua Qian, Jieting Wang and Feijiang Li presents a new evaluation metric, called Pure Accuracy (PA), which seeks to offset the performance of random consistency from the traditional accuracy metric. The authors show that
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