Modeling dependencies among features is fundamental for many machine learning tasks. Although there are often multiple related instances that may be leveraged to inform conditional dependencies, typical approaches only model conditional dependencies over individual instances. In this work, we propose a novel framework, partially observed exchangeable modeling (POEx) that takes in a set of related partially observed instances and infers the conditional distribution for the unobserved dimensions over multiple elements. Our approach jointly models the intra-instance (among features in a point) and inter-instance (among multiple points in a set) dependencies in data. POEx is a general framework that encompasses many existing tasks such as point cloud expansion and few-shot generation, as well as new tasks like few-shot imputation. Despite its generality, extensive empirical evaluations show that our model achieves state-of-the-art performance across a range of applications.
Efficient probabilistic surrogate modeling techniques for partially-observed large-scale dynamical systems
University of Paderborn（帕德博恩大学）；Lamarr Institute for Machine Learning and Artificial Intelligence（拉马尔机器学习和人工智能研究所）；University of Michigan（密歇根大学）；Technical University of Munich（慕尼黑工业大学）；Technical University of Dortmund（多特蒙德工业大学）
Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Discrete-Time Conditional Gaussian Koopman Network
Department of Mechanical Engineering, University of Wisconsin–Madison（机械工程系，威斯康星大学麦迪逊分校）；Department of Mathematics, University of Wisconsin–Madison（数学系，威斯康星大学麦迪逊分校）
Partially Observed Sparse Graphs: The Unknown Sampling Rate is a Tail Index
RIKEN iTHEMS（理化学研究所跨学科理论科学中心）；RIKEN Center for Advanced Intelligence Project (AIP)（理化学研究所先进智能研究中心）；South China University of Technology（华南理工大学）；Columbia University（哥伦比亚大学）
Hybrid twinning using PBDW and DeepONet for the effective state estimation and prediction on partially known systems
Université Paris-Saclay, CentraleSupélec, ENS Paris-Saclay, CNRS, LMPS - Laboratoire de Mécanique Paris-Saclay（巴黎-萨克雷大学、中央超算学院、巴黎-萨克雷高等师范学院、国家科学研究中心、巴黎-萨克雷力学实验室）；Division of Physics and Applied Physics, School of Physical and Mathematical Sciences（物理与应用物理系）；Consiglio Nazionale delle Ricerce, CNR-SPIN（意大利国家研究委员会、CNR-SPIN）
Hybrid Gaussian Process Regression with Temporal Feature Extraction for Partially Interpretable Remaining Useful Life Interval Prediction in Aeroengine Prognostics
