Regularization-Based Methods for Ordinal Quantification
Lamarr Institute for Machine Learning and Artificial Intelligence（拉马尔机器学习与人工智能研究所）；TU Dortmund University（多特蒙德工业大学）；Istituto di Scienza e Tecnologie dell’Informazione, Consiglio Nazionale delle Ricerche（意大利国家研究委员会信息科学与技术研究所）
Mirko Bunse, Alejandro Moreo, Fabrizio Sebastiani, Martin Senz
Quantification, i.e., the task of training predictors of the class prevalence values in sets of unlabeled data items, has received increased attention in recent years. However, most quantification research has concentrated on developing algorithms for binary and multiclass problems in which the classes are not ordered. Here, we study the ordinal case, i.e., the case in which a total order is defined on the set of n>2 classes. We give three main contributions to this field. First, we create and make available two datasets for ordinal quantification (OQ) research that overcome the inadequacies of the previously available ones. Second, we experimentally compare the most important OQ algorithms proposed in the literature so far. To this end, we bring together algorithms proposed by authors from very different research fields, such as data mining and astrophysics, who were unaware of each others' developments. Third, we propose a novel class of regularized OQ algorithms, which outperforms existing algorithms in our experiments. The key to this gain in performance is that our regularization prevents ordinally implausible estimates, assuming that ordinal distributions tend to be smooth in practice. We informally verify this assumption for several real-world applications.
Regression with Comparisons: Escaping the Curse of Dimensionality with Ordinal Information
Machine Learning Department（机器学习系）；Department of Statistics and Data Science（统计与数据科学系）；Auton Lab, The Robotics Institute（奥顿实验室（机器人研究所））；Carnegie Mellon University（卡内基梅隆大学）
在监督学习中，我们通常利用完全标记的数据集来设计函数估计或预测的方法。在许多实际情况下，我们能够以较低的成本获得替代反馈。一个广泛的目标是了解有用的，并设计算法来利用，这种替代反馈。在本文中，我们考虑一个半监督回归设置，在那里我们获得额外的顺序（或比较）信息的未标记的样本。我们考虑不同质量的有序反馈，其中我们有一个完美的排序的样本，一个嘈杂的排序的样本或嘈杂的样本之间的成对比较。我们对这些类型的有序反馈在非参数和线性回归中的有用性进行了精确的量化，表明在许多情况下，可以用非常小的标记集准确地估计底层函数，实际上是\emph{escaping the curse of dimensionality}。我们还提出了下限，建立任务的基本限制，并表明我们的算法是最佳的各种设置。最后，我们提出了新的数据集，证明我们的算法的有效性和实用性，并研究其鲁棒性的各种来源的噪声和模型误指定的广泛的实验。
Geometry-Aware Bayesian Quantification via Compositional Data Analysis
Istituto di Scienza e Tecnologie dell’Informazione Consiglio Nazionale delle Ricerche（国家研究委员会信息科学与技术研究所）；Artificial Intelligence Center University of Oviedo（奥维耶多大学人工智能中心）
Likelihood-guided Regularization in Attention Based Models
Department of Statistics, Virginia Polytechnic Institute and State University（统计学系，弗吉尼亚理工大学和州立大学）；Department of Statistics, Virginia Tech.（统计学系，弗吉尼亚理工大学）
Deep learning with self-supervision and uncertainty regularization to count fish in underwater images
Universitat de Barcelona（巴塞罗那大学）；Universidade Federal de Santa Catarina（圣卡塔琳娜联邦大学）；Max Planck Institute of Animal Behavior（马克斯·普朗克动物行为研究所）；Universidade Federal do Paraná（巴拉那联邦大学）；Witwatersrand University（威特沃特斯兰德大学）；Oregon State University（俄勒冈州立大学）；University of Zurich（苏黎世大学）；Universitat Autonoma de Barcelona（巴塞罗那自治大学）
