2020/7/16 15:15
投稿日:6/2(改訂7/16)
機械学習のトップカンファレンスであるICML2020において、AIPセンターから18本の論文が採択されました。
AIPセンターは、ICML2020のランキングにおいて優秀な組織の1つにあげられています。ICML2020で採択された全1088本の論文の中で、杉山 将センター長が著者別論文数1位(11本)、理研AIPセンターは組織別論文数31位※(国内1位)と大躍進しました。
参照:Sergei Ivanov, ICML 2020. Comprehensive analysis of authors, organizations, and countries. medium. Jun 16, 2020.
※参照記事では、理研AIPセンターの論文数は12本と記載されている。しかし、記事内の定義どおりにカウントすると正しくは16本である。16本とすると、理研AIPセンターの組織別論文数は31位となる。実際には、AIPセンターのメンバーが関わった論文は、以下のように18本である。
[Website] https://icml.cc/Conferences/2020
[Accepted Papers] https://icml.cc/Conferences/2020/AcceptedPapersInitial
- Attacks Which Do Not Kill Training Make Adversarial Learning Stronger
Jingfeng Zhang (National University of Singapore)*
Xu Xilie (Shandong University)
Bo Han (HKBU / RIKEN AIP)
Gang Niu (RIKEN AIP)
Lizhen Cui (ShanDong University)
Masashi Sugiyama (RIKEN AIP / The University of Tokyo)
Mohan Kankanhalli (National University of Singapore) - Variational Imitation Learning with Diverse-quality Demonstrations
Voot Tangkaratt (RIKEN AIP)
Bo Han (HKBU / RIKEN AIP)
Mohammad Emtiyaz Khan (RIKEN AIP)
Masashi Sugiyama (RIKEN AIP / The University of Tokyo) - SIGUA: Forgetting May Make Learning with Noisy Labels More Robust
Bo Han (HKBU / RIKEN AIP)
Gang Niu (RIKEN AIP)
Xingrui Yu (University of Technology Sydney)
Quanming Yao (4Paradigm)
Miao Xu (University of Queensland/ RIKEN AIP)
Ivor Tsang (University of Technology Sydney)
Masashi Sugiyama (RIKEN AIP / The University of Tokyo) - Online Dense Subgraph Discovery via Blurred-Graph Feedback
Yuko Kuroki (The University of Tokyo / RIKEN AIP)
Atsushi Miyauchi (University of Tokyo / RIKEN AIP)
Junya Honda (University of Tokyo / RIKEN AIP)
Masashi Sugiyama (RIKEN AIP / The University of Tokyo) - Few-shot Domain Adaptation by Causal Mechanism Transfer
Takeshi Teshima (The University of Tokyo / RIKEN AIP)
Issei Sato (University of Tokyo / RIKEN AIP)
Masashi Sugiyama (RIKEN AIP / The University of Tokyo) - Fast Deterministic CUR Matrix Decomposition with Accuracy Assurance
Yasutoshi Ida (NTT)
Sekitoshi Kanai (NTT Software Innovation Center)
Yasuhiro Fujiwara (NTT Communication Science Laboratories)
Tomoharu Iwata (NTT)
Koh Takeuchi (NTT)
Hisashi Kashima (Kyoto University / RIKEN AIP) - Learning with Multiple Complementary Labels
Lei Feng (Nanyang Technological University)*
Takuo Kaneko (The University of Tokyo / RIKEN AIP)
Bo Han (HKBU / RIKEN AIP)
Gang Niu (RIKEN AIP)
Bo An (Nanyang Technological University)
Masashi Sugiyama (RIKEN AIP / The University of Tokyo) - Evolutionary Topology Search for Tensor Network Decomposition
Chao Li (RIKEN AIP)
Zhun Sun (RIKEN AIP) - Accelerating the diffusion-based ensemble sampling by non-reversible dynamics
Futoshi Futami (The University of Tokyo/ RIKEN AIP)
Issei Sato (University of Tokyo / RIKEN AIP)
Masashi Sugiyama (RIKEN AIP / The University of Tokyo) - Unbiased Risk Estimators Can Mislead: A Case Study of Learning with Complementary Labels
Yu-Ting Chou (National Taiwan University)*
Gang Niu (RIKEN AIP)
Hsuan-Tien Lin (National Taiwan University)
Masashi Sugiyama (RIKEN AIP / The University of Tokyo) - Searching to Exploit Memorization Effect in Learning with Noisy Labels
Quanming Yao (4Paradigm)
Hansi Yang (Tsinghua)
Bo Han (HKBU / RIKEN AIP)
Gang Niu (RIKEN AIP)
James Kwok (Hong Kong University of Science and Technology) - Normalized Flat Minima: Exploring Scale Invariant Definition of Flat Minima for Neural Networks Using PAC-Bayesian Analysis
Yusuke Tsuzuku (The University of Tokyo / RIKEN AIP)
Issei Sato (University of Tokyo / RIKEN AIP)
Masashi Sugiyama (RIKEN AIP / The University of Tokyo) - Progressive Identification of True Labels for Partial-Label Learning
Jiaqi Lv (Southeast University)*
Miao Xu (University of Queensland/ RIKEN AIP)
Lei Feng (Nanyang Technological University)*
Gang Niu (RIKEN AIP)
Xin Geng (Southeast University)
Masashi Sugiyama (RIKEN AIP / The University of Tokyo) - Graph Homomorphism Convolution
Hoang Nguyen (RIKEN AIP)
Takanori Maehara (RIKEN AIP) - Multi-fidelity Bayesian Optimization with Max-value Entropy Search and its Parallelization
Shion Takeno (Nagoya Institute of Technology)
Hitoshi Fukuoka (Nagoya University)
Yuhki Tsukada (Nagoya University)
Toshiyuki Koyama (Nagoya University)
Motoki Shiga (Gifu University)
Ichiro Takeuchi (Nagoya Institute of Technology / RIKEN AIP)
Masayuki Karasuyama (Nagoya Institute of Technology) - Do We Need Zero Training Loss After Achieving Zero Training Error?
Takashi Ishida (The University of Tokyo / RIKEN AIP)
Ikko Yamane (The University of Tokyo)
Tomoya Sakai (NEC)
Gang Niu (RIKEN AIP)
Masashi Sugiyama (RIKEN AIP / The University of Tokyo) - Handling the Positive-Definite Constraint in the Bayesian Learning Rule
Wu Lin (UBC)+
Mark Schmidt (University of British Columbia)
Mohammad Emtiyaz Khan (RIKEN AIP) - Training Binary Neural Networks using the Bayesian Learning Rule
Xiangming Meng (RIKEN AIP)
Roman Bachmann (EPFL)*
Mohammad Emtiyaz Khan (RIKEN AIP)
*過去に実習生としてAIPセンターに所属。
+過去に理研AIPセンターに所属。現在、理研AIPセンターのPIとの共同指導の下、博士課程に在籍。
+過去に理研AIPセンターに所属。現在、理研AIPセンターのPIとの共同指導の下、博士課程に在籍。
更新:2020/07/06, 2020/6/3
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