Quantification is the supervised learning task that consists of training predictors of the class prevalence values of sets of unlabelled data, and is of special interest when the labelled data on which the predictor has been trained and the unlabelled data are not IID, i.e., suffer from dataset shift. To date, quantification methods have mostly been tested only on a special case of dataset shift, i.e., prior probability shift; the relationship between quantification and other types of dataset shift remains, by and large, unexplored. In this work we carry out an experimental analysis of how current quantification algorithms behave under different types of dataset shift, in order to identify limitations of current approaches and hopefully pave the way for the development of more broadly applicable methods. We do this by proposing a fine-grained taxonomy of types of dataset shift, by establishing protocols for the generation of datasets affected by these types of shift, and by testing existing quantification methods on the datasets thus generated. One finding that results from this investigation is that many existing quantification methods that had been found robust to prior probability shift are not necessarily robust to other types of dataset shift. A second finding is that no existing quantification method seems to be robust enough to dealing with all the types of dataset shift we simulate in our experiments. The code needed to reproduce all our experiments is publicly available at https://github.com/pglez82/quant_datasetshift.
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）
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）
Improving Robustness against Real-World and Worst-Case Distribution Shifts through Decision Region Quantification
University of Erlangen-Nürnberg（埃尔朗根-纽伦堡大学）；Mila - Quebec AI Institute（米拉-魁北克人工智能研究所）；Hausdorff Center for Mathematics, University of Bonn（波恩大学豪斯多夫数学中心）；McGill University（麦吉尔大学）；Google Deepmind（谷歌DeepMind）
A Comparative Evaluation of Quantification Methods
University of Mannheim（曼海姆大学）；RWTH Aachen University（亚琛工业大学）；GESIS - Leibniz Institute for the Social Sciences（GESIS莱布尼茨社会科学研究所）；Complexity Science Hub（复杂性科学中心）；University of Passau（帕绍大学）
Conformal Prediction for Federated Uncertainty Quantification Under Label Shift
Ecole Polytechnique（巴黎综合理工学院）；Lagrange Mathematics and Computing Research Center（拉格朗日数学与计算研究中心）；Skolkovo Institute of Science and Technology（斯科尔科沃科学技术研究院）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Technology Innovation Institute（技术创新研究院）
TAM-Chain: Multi-Scale Thyroid Cytology Classification via Absorbing Markov Chains and Shannon Entropy Uncertainty Quantification for False-Negative Suppression and Domain-Shift Adaptation
Faculty of Mathematics, Mechanics and Informatics, VNU University of Science（越南国立大学理学院数学、力学与信息学系）
Emulating compact binary population synthesis simulations with uncertainty quantification and model comparison using Bayesian normalizing flows
Center for Interdisciplinary Exploration and Research in Astrophysics（跨学科天体物理探索与研究中心）；Northwestern University（西北大学）；The NSF-Simons AI Institute for the Sky (NSF-Simons SkAI)（NSF-模拟斯诺人工智能天空研究所）
Uncertainty Quantification for Atlas-Level Cell Type Transfer
Institute of Computational Biology, Helmholtz Center Munich（慕尼黑亥姆霍兹中心计算生物学研究所）；Department of Mathematics, TU Munich（慕尼黑工业大学数学系）；TUM School of Life Sciences Weihenstephan, Technical University of Munich（慕尼黑工业大学维森斯蒂芬生命科学学院）
Uncertainty quantification in graph-based classification of high dimensional data
University of California Los Angeles（加利福尼亚大学洛杉矶分校）；Caltech（加州理工学院）；University of Edinburgh（爱丁堡大学）
高维数据的分类有着广泛的应用。在许多这些应用中，为所得分类配备不确定性度量可能与分类本身一样重要。在本文中，我们介绍，开发算法，并调查的性质，各种贝叶斯模型的任务，二进制分类，通过后验分布的分类标签，这些方法自动给出的不确定性的措施。这些方法都是基于半监督学习的图形公式。我们提供了一个统一的框架，汇集了各种方法，已被介绍在不同的社区内的数学科学。我们研究了基于图的设置中的概率分类，将贝叶斯逆问题的水平集方法推广到分类设置，并将基于Ginzburg-Landau优化的分类器推广到贝叶斯设置;我们还表明，概率和水平集方法是[Zhu et al 2003]中引入的调和函数方法的自然放松。我们引入了适用于大型数据集的高效数值方法，用于基于MCMC的采样和基于梯度的MAP估计。通过数值实验，我们研究了我们的模型的分类精度和不确定性量化;这些实验展示了一套通常用于评估基于图的半监督学习算法的数据集。
A Unified Perspective on Conformal Prediction and Wasserstein Distributionally Robust Optimization for Uncertainty Quantification
Contextual Robotics Institute, University of California San Diego（加州大学圣迭戈分校语境机器人研究所）；University of Southern California（南加州大学）；California Institute of Technology（加州理工学院）；ETH Zürich（苏黎世联邦理工学院）
