Label space expansion for multi-label classification (MLC) is a methodology that encodes the original label vectors to higher dimensional codes before training and decodes the predicted codes back to the label vectors during testing. The methodology has been demonstrated to improve the performance of MLC algorithms when coupled with off-the-shelf error-correcting codes for encoding and decoding. Nevertheless, such a coding scheme can be complicated to implement, and cannot easily satisfy a common application need of cost-sensitive MLC---adapting to different evaluation criteria of interest. In this work, we show that a simpler coding scheme based on the concept of a reference pair of label vectors achieves cost-sensitivity more naturally. In particular, our proposed cost-sensitive reference pair encoding (CSRPE) algorithm contains cluster-based encoding, weight-based training and voting-based decoding steps, all utilizing the cost information. Furthermore, we leverage the cost information embedded in the code space of CSRPE to propose a novel active learning algorithm for cost-sensitive MLC. Extensive experimental results verify that CSRPE performs better than state-of-the-art algorithms across different MLC criteria. The results also demonstrate that the CSRPE-backed active learning algorithm is superior to existing algorithms for active MLC, and further justify the usefulness of CSRPE.
Mitigating Shortcuts in Language Models with Soft Label Encoding
Stevens Institute of Technology（史蒂文斯理工学院）；Shanghai Jiao Tong University（上海交通大学）；New Jersey Institute of Technology（新泽西理工学院）；University of Georgia（佐治亚大学）
University of Sheffield（谢菲尔德大学）；ELLIS Unit Lisbon（欧洲卓越实验室网络里斯本单元）；Instituto Superior Tecnico（里斯本高等工程学院）；Universidade de Lisboa（里斯本大学）；National Centre for Text Mining（国家文本挖掘中心）；The University of Manchester（曼彻斯特大学）；Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology（日本产业技术综合研究所人工智能研究中心）
Multi-Head Encoding for Extreme Label Classification
Shandong University（山东大学）；School of Information Science and Engineering, Shandong University（山东大学信息科学与工程学院）；School of Control Science and Engineering, Shandong University（山东大学控制科学与工程学院）；School of Qilu Transportation, Shandong University（山东大学齐鲁交通学院）
FedRE: A Representation Entanglement Framework for Model-Heterogeneous Federated Learning
Teleinfo, CAICT（中国信息通信研究院）；Nanyang Technological University（南洋理工大学）；Tsinghua University（清华大学）；Nanjing University of Posts and Telecommunications（南京邮电大学）；University of Texas at Dallas（德克萨斯大学达拉斯分校）；University of British Columbia（不列颠哥伦比亚大学）；China University of Mining and Technology-Beijing（中国矿业大学（北京））；China University of Mining and Technology（中国矿业大学）；Beijing Jiaotong University（北京交通大学）
High-fidelity 3D multi-slab diffusion MRI using Slab-shifting for Harmonized 3D Acquisition and Reconstruction with Profile Encoding Networks (SHARPEN)
PEOPL: Characterizing Privately Encoded Open Datasets with Public Labels
Massachusetts Institute of Technology (MIT)（麻省理工学院）；University of California, Berkeley（加州大学伯克利分校）；University of California, San Francisco（加州大学旧金山分校）；Clemson University（克莱姆森大学）；Maynooth University（梅努斯大学）
