We consider the problem of wisely using a limited budget to label a small subset of a large unlabeled dataset. We are motivated by the NLP problem of word sense disambiguation. For any word, we have a set of candidate labels from a knowledge base, but the label set is not necessarily representative of what occurs in the data: there may exist labels in the knowledge base that very rarely occur in the corpus because the sense is rare in modern English; and conversely there may exist true labels that do not exist in our knowledge base. Our aim is to obtain a classifier that performs as well as possible on examples of each "common class" that occurs with frequency above a given threshold in the unlabeled set while annotating as few examples as possible from "rare classes" whose labels occur with less than this frequency. The challenge is that we are not informed which labels are common and which are rare, and the true label distribution may exhibit extreme skew. We describe an active learning approach that (1) explicitly searches for rare classes by leveraging the contextual embedding spaces provided by modern language models, and (2) incorporates a stopping rule that ignores classes once we prove that they occur below our target threshold with high probability. We prove that our algorithm only costs logarithmically more than a hypothetical approach that knows all true label frequencies and show experimentally that incorporating automated search can significantly reduce the number of samples needed to reach target accuracy levels.
Prototype-Guided Non-Exemplar Continual Learning for Cross-subject EEG Decoding
Dept. of Artificial Intelligence Korea University Seoul, Republic of Korea（人工智能系 韩国大学首尔共和国）；Cognitive Engineering Korea University Seoul, Republic of Korea（认知工程 韩国大学首尔共和国）
Incorporating uncertainty quantification into travel mode choice modeling: a Bayesian neural network (BNN) approach and an uncertainty-guided active survey framework
PCA-Guided Autoencoding for Structured Dimensionality Reduction in Active Infrared Thermography
Khalifa University of Science and Technology（卡利法科技科学大学）；Advanced Research and Innovation Center（先进研究与创新中心）；Department of Aerospace Engineering（航空航天工程系）；BioMedIA Lab（BioMedIA实验室）；Mohamed Bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Department of Electrical Engineering and Computer Science（电气工程与计算机科学系）；Department of Information Systems and Operations Research（信息系统与运营管理系）；Vienna University of Economics and Business（维也纳经济与商业大学）；Department of Industrial and Information Engineering and Economics (DIIIE)（工业与信息工程与经济学系）；University of L’Aquila（拉奎拉大学）；Department of Industrial and Systems Engineering（工业与系统工程系）
Active Learning-Guided Seq2Seq Variational Autoencoder for Multi-target Inhibitor Generation
Barcelona Supercomputing Center (BSC)（巴塞罗那超级计算中心）；PhD program in Biotechnology, Faculty of Pharmacy and Food Sciences, University of Barcelona (UB)（巴塞罗那大学药学与食品科学系生物技术博士项目）；Alzheimer's Research UK Oxford Drug Discovery Institute, Centre for Medicines Discovery, Nuffield Department of Medicine, Oxford, UK（英国阿尔茨海默病研究奥德赛药物发现研究所、药物发现中心、医学系，牛津，英国）；Centre for Medicines Discovery, Nuffield Department of Medicine Research Building (NDMRB), University of Oxford, Oxford, UK（药物发现中心、医学系研究大楼（NDMRB）、牛津大学，牛津，英国）
