Double logistic regression approach to biased positive-unlabeled data
University of Life Sciences, Warsaw（华沙生命科学大学）；Polish Academy of Sciences（波兰科学院）；Warsaw University of Technology（华沙理工大学）；Nicolaus Copernicus University（哥白尼大学）
Konrad Furmańczyk, Jan Mielniczuk, Wojciech Rejchel, Paweł Teisseyre
Positive and unlabelled learning is an important problem which arises naturally in many applications. The significant limitation of almost all existing methods lies in assuming that the propensity score function is constant (SCAR assumption), which is unrealistic in many practical situations. Avoiding this assumption, we consider parametric approach to the problem of joint estimation of posterior probability and propensity score functions. We show that under mild assumptions when both functions have the same parametric form (e.g. logistic with different parameters) the corresponding parameters are identifiable. Motivated by this, we propose two approaches to their estimation: joint maximum likelihood method and the second approach based on alternating maximization of two Fisher consistent expressions. Our experimental results show that the proposed methods are comparable or better than the existing methods based on Expectation-Maximisation scheme.
Revisiting Sample Selection Approach to Positive-Unlabeled Learning: Turning Unlabeled Data into Positive rather than Negative
RIKEN Center for Advanced Intelligence Project（理化学研究所先进智能中心）；Center for Artificial Intelligence, University of Technology Sydney（悉尼科技大学人工智能中心）；University of Tokyo（东京大学）
A Robust AUC Maximization Framework with Simultaneous Outlier Detection and Feature Selection for Positive-Unlabeled Classification
University of Rochester（罗切斯特大学）；Baylor College of Medicine（贝勒医学院）；University of Texas Health Science Center at Houston（休斯顿德克萨斯大学健康科学中心）；University of Washington（华盛顿大学）
Toward Deep Supervised Anomaly Detection: Reinforcement Learning from Partially Labeled Anomaly Data
Australian Institute for Machine Learning（澳大利亚机器学习研究所）；The University of Adelaide（阿德莱德大学）；Advanced Analytics Institute（高级分析研究所）；University of Technology Sydney（悉尼科技大学）
Adv-SSL: Adversarial Self-Supervised Representation Learning with Theoretical Guarantees
School of Mathematics and Statistics, Wuhan University（武汉大学数学与统计学学院）；School of Artificial Intelligence, Wuhan University（武汉大学人工智能学院）；National Center for Applied Mathematics in Hubei, Wuhan University（湖北省应用数学中心）；Hubei Key Laboratory of Computational Science, Wuhan University（湖北省计算科学重点实验室）；Center of Statistical Research, Southwestern University of Finance and Economics（西南财经大学统计研究中心）；School of Statistics and Data Science, Southwestern University of Finance and Economics（西南财经大学统计与数据科学学院）；New Cornerstone Science Laboratory, Southwestern University of Finance and Economics（西南财经大学新基石科学实验室）；Institute for Math & AI, Wuhan University（武汉大学数学与人工智能研究院）
A Graph-Based Approach for Active Learning in Regression
University of California, Davis（加利福尼亚大学戴维斯分校）；Biocomplexity Institute and Initiative, University of Virginia（弗吉尼亚大学生物复杂性研究所及计划）；University at Albany – SUNY（纽约州立大学奥尔巴尼分校）
