The problem of representative selection amounts to sampling few informative exemplars from large datasets. This paper presents MOSAIC, a novel representative selection approach from high-dimensional data that may exhibit non-linear structures. Resting upon a novel quadratic formulation, Our method advances a multi-criteria selection approach that maximizes the global representation power of the sampled subset, ensures diversity, and rejects disruptive information by effectively detecting outliers. Through theoretical analyses we characterize the obtained sketch and reveal that the sampled representatives maximize a well-defined notion of data coverage in a transformed space. In addition, we present a highly scalable randomized implementation of the proposed algorithm shown to bring about substantial speedups. MOSAIC's superiority in achieving the desired characteristics of a representative subset all at once while exhibiting remarkable robustness to various outlier types is demonstrated via extensive experiments conducted on both real and synthetic data with comparisons to state-of-the-art algorithms.
Lexicographic optimization-based approaches to learning a representative model for multi-criteria sorting with non-monotonic criteria
School of Economics and Management, Dalian University of Technology（大连理工大学经济管理学院）；School of Data Science and Artificial Intelligence, Dongbei University of Finance and Economics（东北财经大学数据科学与人工智能学院）
Probabilistic data quality assessment for structural monitoring data via outlier-resistant conditional diffusion model
Key Lab of Smart Prevention and Mitigation of Civil Engineering Disasters of the Ministry of Industry and Information Technology（工业和信息化部智能防灾减灾重点实验室）；School of Civil Engineering, Harbin Institute of Technology（哈尔滨工业大学土木工程学院）；Key Lab of Structures Dynamic Behavior and Control of the Ministry of Education（教育部结构动力行为与控制重点实验室）；Harbin Institute of Technology（哈尔滨工业大学）
Representative Task Self-selection for Flexible Clustered Lifelong Learning
Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences（中国科学院机器人与智能制造创新研究院）；University of Chinese Academy of Sciences（中国科学院大学）；Northeastern University（东北大学）；Xidian University（西安电子科技大学）；Huaqiao University（华侨大学）
Outlier Synthesis via Hamiltonian Monte Carlo for Out-of-Distribution Detection
Huazhong University of Science and Technology（华中科技大学）；School of Computer Science and Technology（计算机科学与技术学院）；National Engineering Research Center for Big Data Technology and System（国家大数据技术与系统工程研究中心）
Information-theoretic Online Memory Selection for Continual Learning
DeepMind（深度思维）；University of Toronto（多伦多大学）；Vector Institute（向量研究所）；Google Brain（谷歌大脑）；Baidu Apollo（百度阿波罗）
无任务持续学习中的一个挑战性问题是从数据流中在线选择代表性重放记忆。在这项工作中，我们从信息论的角度研究了在线记忆选择问题。为了收集最多的信息，我们提出了\textit{surprise}和\textit{learnability}标准来挑选信息点并避免离群值。我们提出了一个贝叶斯模型来计算的标准，有效地利用秩一矩阵结构。我们证明，这些标准鼓励选择信息点在线记忆选择的贪心算法。此外，通过识别\textit{the timing to update the memory}的重要性，我们介绍了随机信息理论水库采样器（InfoRS），它进行高信息的选择点之间的采样。与水库采样相比，InfoRS显示出对数据不平衡的更好的鲁棒性。最后，通过对持续学习基准的实证分析，证明了该方法的有效性。
ORSA: Outlier Robust Stacked Aggregation for Best- and Worst-Case Approximations of Ensemble Systems\
University of Stuttgart（斯图加特大学）；Advantest Europe GmbH（爱德万测试欧洲有限公司）
近年来，集成学习在应用中的使用显著增长，这是由于计算能力的增加允许在合理的时间范围内训练大型集成。许多应用，例如，恶意软件检测、面部识别或财务决策使用有限的学习算法集合，并且以获得比任何其它单独的学习算法更好的预测性能的方式聚集它们。在半导体器件的硅后验证（Post-Silicon Validation for Semiconductor Devices，PSV）领域中，通常提供由各种器件组成的数据集，例如，不同生产线的芯片。在PSV中，任务是使用多个学习算法来近似数据的底层函数，每个学习算法都在特定于设备的子集上进行训练，而不是提高任意分类器在整个数据集上的性能。此外，期望是未知数量的子集描述显示非常不同特征的函数。相应的系综成员，被称为离群值，可以严重影响近似。我们的方法旨在找到一个合适的近似值，该近似值对离群值具有鲁棒性，并以适用于尽可能多的类型的方式代表最佳或最差情况。使用“soft-max”或“soft-min”函数代替最大值或最小值运算符。一个神经网络（NN）被训练成在两个阶段的过程中学习这个“软函数”。首先，我们选择代表最佳或最差情况的集合成员的子集。其次，我们结合这些成员，并定义一个权重，使用本地离群值因子（LOF）的属性，以增加非离群值的影响，并减少离群值。加权确保了对离群值的鲁棒性，并确保近似值适用于大多数类型。
Genie: A new, fast, and outlier-resistant hierarchical clustering algorithm
Systems Research Institute, Polish Academy of Sciences（波兰科学院系统研究所）；Faculty of Mathematics and Information Science, Warsaw University of Technology（华沙理工大学数学与信息科学学院）；Institute of Computer Science, Polish Academy of Sciences（波兰科学院计算机科学研究所）
Selection of a representative sorting model in a preference disaggregation setting: a review of existing procedures, new proposals, and experimental comparison
MapTab: A Diagnostic Benchmark for Long-Horizon Multi-Criteria Multimodal Reasoning on Heterogeneous Topological Graphs
National Key Laboratory for Novel Software Technology, Nanjing University（南京大学新型软件技术国家重点实验室）；School of Intelligence Science and Technology, Nanjing University（南京大学智能科学与技术学院）；AMAP, Alibaba Group（阿里集团AMAP）；School of Computing and Artificial Intelligence, Southwest Jiaotong University（西南交通大学计算机与人工智能学院）
NIPS - Not Even Wrong? A Systematic Review of Empirically Complete Demonstrations of Algorithmic Effectiveness in the Machine Learning and Artificial Intelligence Literature
The Alan Turing Institute（艾伦·图灵研究所）；University College London（伦敦大学学院）；University of Warwick（华威大学）；Kings College Hospital（国王学院医院）
How to Score Experts for One-Shot MoE Expert Pruning: A Systematic Formulation and Selection Principle
Zhejiang University（浙江大学）；Westlake University（西湖大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Carnegie Mellon University（卡内基梅隆大学）
Winning the Big Data Technologies Horizon Prize: Fast and reliable forecasting of electricity grid traffic by identification of recurrent fluctuations
Biofisika Institute (CSIC, UPV/EHU)（生物物理研究所（西班牙国家研究委员会、巴斯克大学））；University of the Basque Country (UPV/EHU)（巴斯克大学）；IKERBASQUE, Basque Foundation for Science（巴斯克科学基金会（IKERBASQUE））
