Imagen de Portada

Active Learning of Model Discrepancy with Bayesian Experimental Design

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Yang, Huchen, Chen, Chuanqi, Wu, Jin-Long
Formato:	Preprint
Publicado:	2025
Materias:	Machine Learning
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Bayesian Experimental Design for Model Discrepancy Calibration: An Auto-Differentiable Ensemble Kalman Inversion Approach
por: Yang, Huchen, et al.
Publicado: (2025)

Bayesian Experimental Design for Model Discrepancy Calibration: A Rivalry between Kullback--Leibler Divergence and Wasserstein Distance
por: Yang, Huchen, et al.
Publicado: (2026)

Active Learning for Communication Structure Optimization in LLM-Based Multi-Agent Systems
por: Yang, Huchen, et al.
Publicado: (2026)

Stochastic and Non-local Closure Modeling for Nonlinear Dynamical Systems via Latent Score-based Generative Models
por: Dong, Xinghao, et al.
Publicado: (2025)

Neural Dynamical Operator: Continuous Spatial-Temporal Model with Gradient-Based and Derivative-Free Optimization Methods
por: Chen, Chuanqi, et al.
Publicado: (2023)

CGNSDE: Conditional Gaussian Neural Stochastic Differential Equation for Modeling Complex Systems and Data Assimilation
por: Chen, Chuanqi, et al.
Publicado: (2024)

Synergizing Transport-Based Generative Models and Latent Geometry for Stochastic Closure Modeling
por: Dong, Xinghao, et al.
Publicado: (2026)

CGKN: A Deep Learning Framework for Modeling Complex Dynamical Systems and Efficient Data Assimilation
por: Chen, Chuanqi, et al.
Publicado: (2024)

Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Discrete-Time Conditional Gaussian Koopman Network
por: Chen, Chuanqi, et al.
Publicado: (2025)

Bayesian experimental design: grouped geometric pooled posterior via ensemble Kalman methods
por: Yang, Huchen, et al.
Publicado: (2026)

Data-Driven Stochastic Closure Modeling via Conditional Diffusion Model and Neural Operator
por: Dong, Xinghao, et al.
Publicado: (2024)

CEBoosting: Online Sparse Identification of Dynamical Systems with Regime Switching by Causation Entropy Boosting
por: Chen, Chuanqi, et al.
Publicado: (2023)

Active MRI Acquisition with Diffusion Guided Bayesian Experimental Design
por: Iollo, Jacopo, et al.
Publicado: (2025)

Gradient-Discrepancy Acquisition for Pool-Based Active Learning
por: Khosravani, Mohamadsadegh, et al.
Publicado: (2026)

Matched-Pair Experimental Design with Active Learning
por: Li, Weizhi, et al.
Publicado: (2025)

Using Low-Discrepancy Points for Data Compression in Machine Learning: An Experimental Comparison
por: Göttlich, Simone, et al.
Publicado: (2024)

Fair Bayesian Data Selection via Generalized Discrepancy Measures
por: Zhang, Yixuan, et al.
Publicado: (2025)

Diffusion Active Learning: Towards Data-Driven Experimental Design in Computed Tomography
por: Barba, Luis, et al.
Publicado: (2025)

Bayesian Experimental Design via Contrastive Diffusions
por: Iollo, Jacopo, et al.
Publicado: (2024)

Amortized Bayesian Experimental Design for Decision-Making
por: Huang, Daolang, et al.
Publicado: (2024)

Bayesian Active Learning By Distribution Disagreement
por: Werner, Thorben, et al.
Publicado: (2025)

Bayesian Active Learning for Censored Regression
por: Hüttel, Frederik Boe, et al.
Publicado: (2024)

Loss-Driven Bayesian Active Learning
por: Huang, Zhuoyue, et al.
Publicado: (2026)

Self-Correcting Bayesian Optimization through Bayesian Active Learning
por: Hvarfner, Carl, et al.
Publicado: (2023)

Using Machine Learning in Analyzing Air Quality Discrepancies of Environmental Impact
por: Wang, Shuangbao Paul, et al.
Publicado: (2025)

Accelerated Bayesian Optimal Experimental Design via Conditional Density Estimation and Informative Data
por: Huang, Miao, et al.
Publicado: (2025)

Multi-Label Bayesian Active Learning with Inter-Label Relationships
por: Qi, Yuanyuan, et al.
Publicado: (2024)

Experimental Design for Active Transductive Inference in Large Language Models
por: Mukherjee, Subhojyoti, et al.
Publicado: (2024)

Sequential Bayesian Optimal Experimental Design in Infinite Dimensions via Policy Gradient Reinforcement Learning
por: Shen, Kaichen, et al.
Publicado: (2026)

Cost-effective Reduced-Order Modeling via Bayesian Active Learning
por: Rahmati, Amir Hossein, et al.
Publicado: (2025)

Robust Experimental Design via Generalised Bayesian Inference
por: Barlas, Yasir Zubayr, et al.
Publicado: (2025)

Bayesian Active Learning for Discrete Latent Variable Models
por: Jha, Aditi, et al.
Publicado: (2022)

Goal-Oriented Sequential Bayesian Experimental Design for Causal Learning
por: Zhang, Zheyu, et al.
Publicado: (2025)

Informed Initialization for Bayesian Optimization and Active Learning
por: Hvarfner, Carl, et al.
Publicado: (2025)

Bayesian Active Learning in the Presence of Nuisance Parameters
por: Sloman, Sabina J., et al.
Publicado: (2023)

Towards Intrinsic Self-Correction Enhancement in Monte Carlo Tree Search Boosted Reasoning via Iterative Preference Learning
por: Jiang, Huchen, et al.
Publicado: (2024)

Bayesian Design Principles for Offline-to-Online Reinforcement Learning
por: Hu, Hao, et al.
Publicado: (2024)

Learning Dynamic Representations via An Optimally-Weighted Maximum Mean Discrepancy Optimization Framework for Continual Learning
por: Huang, KaiHui, et al.
Publicado: (2025)

PAC Apprenticeship Learning with Bayesian Active Inverse Reinforcement Learning
por: Bajgar, Ondrej, et al.
Publicado: (2025)

Deep Bayesian Active Learning for Preference Modeling in Large Language Models
por: Melo, Luckeciano C., et al.
Publicado: (2024)