Probabilistic Machine Learning for Noisy Labels in Earth Observation
Orion Lab（猎户座实验室）；National Observatory of Athens（雅典国家天文台）；National Technical University of Athens（雅典国立技术大学）；Image Processing Laboratory (IPL), Universitat de València（瓦伦西亚大学图像处理实验室）；Harokopio University of Athens（哈罗科皮奥大学）；Archimedes, Athena Research Center（雅典娜研究中心阿基米德分部）
Spyros Kondylatos, Nikolaos Ioannis Bountos, Ioannis Prapas, Angelos Zavras, Gustau Camps-Valls, Ioannis Papoutsis
Label noise poses a significant challenge in Earth Observation (EO), often degrading the performance and reliability of supervised Machine Learning (ML) models. Yet, given the critical nature of several EO applications, developing robust and trustworthy ML solutions is essential. In this study, we take a step in this direction by leveraging probabilistic ML to model input-dependent label noise and quantify data uncertainty in EO tasks, accounting for the unique noise sources inherent in the domain. We train uncertainty-aware probabilistic models across a broad range of high-impact EO applications-spanning diverse noise sources, input modalities, and ML configurations-and introduce a dedicated pipeline to assess their accuracy and reliability. Our experimental results show that the uncertainty-aware models consistently outperform the standard deterministic approaches across most datasets and evaluation metrics. Moreover, through rigorous uncertainty evaluation, we validate the reliability of the predicted uncertainty estimates, enhancing the interpretability of model predictions. Our findings emphasize the importance of modeling label noise and incorporating uncertainty quantification in EO, paving the way for more accurate, reliable, and trustworthy ML solutions in the field.
UPLME: Uncertainty-Aware Probabilistic Language Modelling for Robust Empathy Regression
School of Electrical Engineering, Computing and Mathematical Sciences, Curtin University（电气工程、计算与数学科学学院，科廷大学）；North South University（北南大学）；BRAC University（布拉克大学）；The Australian National University（澳大利亚国立大学）；University of ÓBuda（奥布达大学）
Uncertainty quantification for probabilistic machine learning in earth observation using conformal prediction
Stellenbosch University（斯泰伦博斯大学）；The Nature Conservancy（大自然保护协会）；University of Cape Town（开普敦大学）；Norwegian Institute for Nature Research—NINA（挪威自然研究所—NINA）；Jet Propulsion Laboratory（喷气推进实验室）；California Institute of Technology（加州理工学院）；South African Environmental Observation Network（南非环境观测网络）
Probabilistic Random Forest: A machine learning algorithm for noisy datasets
Tel-Aviv University（特拉维夫大学）
机器学习（ML）算法在天文数据分析中变得越来越重要。然而，由于大多数ML算法的设计没有考虑到数据的不确定性，基于ML的研究主要限于高信噪比的数据。如此高质量的天文数据集并不常见。在这项工作中，我们修改了长期建立的随机森林（RF）算法，以考虑测量中的不确定性（即，特征）以及在所分配的类中（即，标签）。为此，概率随机森林（Probably Random Forest，PRF）算法将特征和标签视为概率分布函数，而不是确定性数量。我们进行了各种实验，我们注入不同类型的噪声的数据集，并比较的准确性的PRF的RF。PRF在所有情况下都优于RF，运行时间适度增加。我们发现，在噪声特征的情况下，分类精度提高了10%，在噪声标签的情况下，提高了30%。与干净的数据集相比，对于具有多达45%错误分类对象的数据集，PRF准确度降低了不到5%。除了提高噪声数据集的预测精度外，PRF还能自然地处理数据中的缺失值，并且在应用于训练集和测试集中具有不同噪声特征的数据集时，其性能优于RF，这表明它可以用于迁移学习。
Real-time probabilistic tsunami forecasting via generative AI
Fujitsu Research, Fujitsu Limited（富士通研究所，富士通株式会社）；Earthquake Research Institute, The University of Tokyo（东京大学地震研究所）；International Research Institute of Disaster Science, Tohoku University（东北大学国际灾害科学研究所）
A probabilistic latent variable model for detecting structure in binary data
University of California Berkeley（加州大学伯克利分校）；Duke University School of Medicine（杜克大学医学院）；Beth Israel Deaconess Medical Center（贝斯以色列女执事医疗中心）；Harvard Medical School（哈佛医学院）；Intel Labs（英特尔实验室）
How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data?
Department of Physics and Astronomy, University of Bologna（博洛尼亚大学物理与天文学系）；Max Planck Institute for Intelligent Systems, Cyber Valley（智能系统马克斯·普朗克研究所，Cyber Valley）；Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology（代尔夫特理工大学电子工程、数学与计算机科学学院）
Credibility-Aware Multi-Modal Fusion Using Probabilistic Circuits
Erik Jonsson School of Engineering & Computer Science, The University of Texas at Dallas（德克萨斯大学达拉斯分校埃里克·琼森工程与计算机科学学院）；Air Force Research Laboratory（空军研究实验室）；TU Darmstadt（达姆施塔特工业大学）
