How should a firm allocate its limited interviewing resources to select the optimal cohort of new employees from a large set of job applicants? How should that firm allocate cheap but noisy resume screenings and expensive but in-depth in-person interviews? We view this problem through the lens of combinatorial pure exploration (CPE) in the multi-armed bandit setting, where a central learning agent performs costly exploration of a set of arms before selecting a final subset with some combinatorial structure. We generalize a recent CPE algorithm to the setting where arm pulls can have different costs and return different levels of information. We then prove theoretical upper bounds for a general class of arm-pulling strategies in this new setting. We apply our general algorithm to a real-world problem with combinatorial structure: incorporating diversity into university admissions. We take real data from admissions at one of the largest US-based computer science graduate programs and show that a simulation of our algorithm produces a cohort with hiring overall utility while spending comparable budget to the current admissions process at that university.
Cross-cohort TB classification using clinical data gathered in Uganda and South Africa
University of Stellenbosch（斯泰伦博斯大学）；Stellenbosch University（斯泰伦博斯大学）；University of Amsterdam（阿姆斯特丹大学）；University of Göttingen（哥廷根大学）；Makerere University（马凯雷雷大学）
A novel interpretable machine learning system to generate clinical risk scores: An application for predicting early mortality or unplanned readmission in a retrospective cohort study
Duke-NUS Medical School（杜克-新加坡国立大学医学院）；Singapore Health Services（新加坡保健服务集团）；Singapore General Hospital（新加坡中央医院）；National University of Singapore（新加坡国立大学）；Duke University（杜克大学）；Singapore Eye Research Institute（新加坡眼科研究所）；Singapore National Eye Centre（新加坡国家眼科中心）
Bias in Surface Electromyography Features across a Demographically Diverse Cohort
Graduate Group in Computer Science, University of California, Davis（计算机科学研究生组，加州大学戴维斯分校）；Department of Electrical and Computer Engineering, University of California, Davis（电气与计算机工程系，加州大学戴维斯分校）；Department of Mechanical and Mechatronic Engineering and Advanced Manufacturing, California State University, Chico（机械与机电工程及先进制造系，加州州立大学奇科分校）；Department of Neurobiology, Physiology & Behavior, University of California, Davis（神经生物学、生理学与行为系，加州大学戴维斯分校）；Department of Otolaryngology / Head & Neck Surgery, University of California, Davis（耳鼻喉科/头颈外科系，加州大学戴维斯分校）；Center for Mind & Brain, University of California, Davis（心智与大脑中心，加州大学戴维斯分校）；Department of Mechanical & Aerospace Engineering, University of California, Davis（机械与航空航天工程系，加州大学戴维斯分校）
Data distribution impacts the performance and generalisability of contrastive learning-based foundation models of electrocardiograms
National Heart and Lung Institute, Imperial College London（伦敦帝国理工学院国家心肺研究所）；Chelsea and Westminster Hospital NHS Foundation Trust（切尔西和威斯敏斯特医院NHS基金会信托）；Zhongshan Hospital of Fudan University（复旦大学附属中山医院）；Shanghai Institute of Cardiovascular Diseases（上海市心血管病研究所）；National Clinical Research Centre for Interventional Medicine（国家介入医学临床研究中心）；Uppsala University（乌普萨拉大学）；Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center（范德堡大学医学中心临床与转化研究所）；Center for Digital Genomic Medicine, Department of Medicine, Vanderbilt University Medical Center（范德堡大学医学中心数字基因组医学中心）；Division of Cardiovascular Medicine, Vanderbilt University Medical Center（范德堡大学医学中心心血管内科）
Enhancing Semi-Supervised Learning via Representative and Diverse Sample Selection
BNU-HKBU United International College（北京师范大学-香港浸会大学联合国际学院）；WeDoctor Cloud（微医云）；The State Key Laboratory of Blockchain and Data Security, Zhejiang University（浙江大学区块链与数据安全国家重点实验室）
PNCS:Power-Norm Cosine Similarity for Diverse Client Selection in Federated Learning
Department of Electrical and Computer Engineering, McMaster University, Hamilton, Canada（电气与计算机工程系，麦斯特大学）；Department of Electrical and Computer Engineering, National Yang-Ming Chiao-Tung University, Taiwan（电气与计算机工程系，国立阳明交通大学）
At-Admission Prediction of Mortality and Pulmonary Embolism in COVID-19 Patients Using Statistical and Machine Learning Methods: An International Cohort Study
University of Oxford（牛津大学）；Institute for Clinical Research, National Institutes of Health (NIH), Malaysia（马来西亚国立卫生研究院临床研究所）；Amsterdam University Medical Center（阿姆斯特丹大学医学中心）
Graph-Based Active Machine Learning Method for Diverse and Novel Antimicrobial Peptides Generation and Selection
Mila Quebec AI Institute（米拉魁北克人工智能研究所）；Jacobs University Bremen（不来梅雅各布大学）；Institute of Immunology and Cancer（免疫学与癌症研究所）；Canadian Institute for Advanced Research (CIFAR)（加拿大高等研究院（CIFAR））
