Ensemble-Conditional Gaussian Processes (Ens-CGP): Representation, Geometry, and Inference
Earth Signals and Systems Group（地球信号与系统组）；Earth, Atmospheric and Planetary Sciences（地球、大气与行星科学）；Massachusetts Institute of Technology（麻省理工学院）
Sai Ravela, Jae Deok Kim, Kenneth Gee, Xingjian Yan, Samson Mercier, Lubna Albarghouty, Anamitra Saha
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20 pages. Technical manuscrupt on representational equivalence between conditional Gaussian inference, quadratic optimization, and RKHS geometry in finite dimensions
We formulate Ensemble-Conditional Gaussian Processes (Ens-CGP), a finite-dimensional synthesis that centers ensemble-based inference on the conditional Gaussian law. Conditional Gaussian processes (CGP) arise directly from Gaussian processes under conditioning and, in linear-Gaussian settings, define the full posterior distribution for a Gaussian prior and linear observations. Classical Kalman filtering is a recursive algorithm that computes this same conditional law under dynamical assumptions; the conditional Gaussian law itself is therefore the underlying representational object, while the filter is one computational realization. In this sense, CGP provides the probabilistic foundation for Kalman-type methods as well as equivalent formulations as a strictly convex quadratic program (MAP estimation), RKHS-regularized regression, and classical regularization. Ens-CGP is the ensemble instantiation of this object, obtained by treating empirical ensemble moments as a (possibly low-rank) Gaussian prior and performing exact conditioning. By separating representation (GP -> CGP -> Ens-CGP) from computation (Kalman filters, EnKF variants, and iterative ensemble schemes), the framework links an earlier-established representational foundation for inference to ensemble-derived priors and clarifies the relationships among probabilistic, variational, and ensemble perspectives.
Revisiting Kernel Attention with Correlated Gaussian Process Representation
FPT Software AI Center（FPT软件AI中心）；School of Electrical Engineering and Computer Science, Washington State University（华盛顿州立大学电气工程与计算机科学学院）；National University of Singapore（新加坡国立大学）
Latent-Constrained Conditional VAEs for Augmenting Large-Scale Climate Ensembles
Hong Kong Polytechnic University（香港理工大学）；Georgia Institute of Technology（佐治亚理工学院）；Los Alamos National Laboratory（洛斯阿拉莫斯国家实验室）；TomTom North America Inc.（TomTom北美公司）
CoDiCast: Conditional Diffusion Model for Global Weather Prediction with Uncertainty Quantification
Florida International University（佛罗里达国际大学）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Hurricane Research Division, National Oceanic and Atmospheric Administration（国家海洋和大气管理局飓风研究 division）
Predicting Critical Heat Flux with Uncertainty Quantification and Domain Generalization Using Conditional Variational Autoencoders and Deep Neural Networks
Conditional Flow Matching for Single-Neuron Electrophysiology: Capturing Multimodal Responses Across Stimuli
University of Cambridge（剑桥大学）；Cedars-Sinai Medical Center（西达赛奈医疗中心）；California Institute of Technology（加州理工学院）；Archimedes AI, Athena Research Center（阿基米德人工智能，雅典娜研究中心）
BEACON: Bayesian Experimental design Acceleration with Conditional Normalizing flows $-$ a case study in optimal monitor well placement for CO$_2$ sequestration
Neural Conditional Probability for Uncertainty Quantification
CSML, Istituto Italiano di Tecnologia（CSML，意大利技术研究院）；University of Novi Sad（诺维萨大学）；CMAP-Ecole Polytechnique（CMAP-巴黎高等学院）；AI Centre, University College London（伦敦大学学院人工智能中心）
