Saved in:
| Main Author: | Young, Robin |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2605.21798 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Amortized Variational Inference for Deep Gaussian Processes
by: Meng, Qiuxian, et al.
Published: (2024)
by: Meng, Qiuxian, et al.
Published: (2024)
Characterizing the Representational Capacity of Neural Processes
by: Young, Robin
Published: (2026)
by: Young, Robin
Published: (2026)
On the Conditioning Consistency Gap in Conditional Neural Processes
by: Young, Robin
Published: (2026)
by: Young, Robin
Published: (2026)
Neural Methods for Amortized Inference
by: Zammit-Mangion, Andrew, et al.
Published: (2024)
by: Zammit-Mangion, Andrew, et al.
Published: (2024)
Neural Processes Maintain Calibrated Biomass Estimates Across Spatiotemporal Gaps and Disturbance
by: Young, Robin, et al.
Published: (2026)
by: Young, Robin, et al.
Published: (2026)
Amortized Network Intervention to Steer the Excitatory Point Processes
by: Song, Zitao, et al.
Published: (2023)
by: Song, Zitao, et al.
Published: (2023)
Amortized Bayesian Local Interpolation NetworK: Fast covariance parameter estimation for Gaussian Processes
by: Feng, Brandon R., et al.
Published: (2024)
by: Feng, Brandon R., et al.
Published: (2024)
Amortized Factor Inference Networks for Posterior Inference
by: Ko, Joohwan, et al.
Published: (2026)
by: Ko, Joohwan, et al.
Published: (2026)
Sparse Gaussian Neural Processes
by: Rochussen, Tommy, et al.
Published: (2025)
by: Rochussen, Tommy, et al.
Published: (2025)
Instrumental and Proximal Causal Inference with Gaussian Processes
by: Zhang, Yuqi, et al.
Published: (2026)
by: Zhang, Yuqi, et al.
Published: (2026)
Sparse Orthogonal Variational Inference for Gaussian Processes
by: Shi, Jiaxin, et al.
Published: (2019)
by: Shi, Jiaxin, et al.
Published: (2019)
Amortized Variational Inference: When and Why?
by: Margossian, Charles C., et al.
Published: (2023)
by: Margossian, Charles C., et al.
Published: (2023)
Sequential Inference for Gaussian Processes: A Signal Processing Perspective
by: Waxman, Daniel, et al.
Published: (2026)
by: Waxman, Daniel, et al.
Published: (2026)
Neural Operator Variational Inference based on Regularized Stein Discrepancy for Deep Gaussian Processes
by: Xu, Jian, et al.
Published: (2023)
by: Xu, Jian, et al.
Published: (2023)
Bayesian Causal Inference with Gaussian Process Networks
by: Giudice, Enrico, et al.
Published: (2024)
by: Giudice, Enrico, et al.
Published: (2024)
Amortized Simulation-Based Inference in Generalized Bayes via Neural Posterior Estimation
by: Sun, Shiyi, et al.
Published: (2026)
by: Sun, Shiyi, et al.
Published: (2026)
Sensitivity-Aware Amortized Bayesian Inference
by: Elsemüller, Lasse, et al.
Published: (2023)
by: Elsemüller, Lasse, et al.
Published: (2023)
Lightweight Gaussian Process Inference in C++ on Metal and CUDA
by: Fang, Yu-Hsueh
Published: (2026)
by: Fang, Yu-Hsueh
Published: (2026)
Exact Inference for Continuous-Time Gaussian Process Dynamics
by: Ensinger, Katharina, et al.
Published: (2023)
by: Ensinger, Katharina, et al.
Published: (2023)
Diffusion Bridge Variational Inference for Deep Gaussian Processes
by: Xu, Jian, et al.
Published: (2025)
by: Xu, Jian, et al.
Published: (2025)
Unsupervised Continual Learning for Amortized Bayesian Inference
by: Mishra, Aayush, et al.
Published: (2026)
by: Mishra, Aayush, et al.
Published: (2026)
Learning Decision Trees as Amortized Structure Inference
by: Mahfoud, Mohammed, et al.
Published: (2025)
by: Mahfoud, Mohammed, et al.
Published: (2025)
Amortized Probabilistic Conditioning for Optimization, Simulation and Inference
by: Chang, Paul E., et al.
Published: (2024)
by: Chang, Paul E., et al.
Published: (2024)
Scalable Bayesian Inference in the Era of Deep Learning: From Gaussian Processes to Deep Neural Networks
by: Antoran, Javier
Published: (2024)
by: Antoran, Javier
Published: (2024)
Gaussian Process Neural Additive Models
by: Zhang, Wei, et al.
Published: (2024)
by: Zhang, Wei, et al.
Published: (2024)
Amortized Inference for Correlated Discrete Choice Models via Equivariant Neural Networks
by: Huch, Easton, et al.
Published: (2026)
by: Huch, Easton, et al.
Published: (2026)
Detecting Model Misspecification in Amortized Bayesian Inference with Neural Networks: An Extended Investigation
by: Schmitt, Marvin, et al.
Published: (2024)
by: Schmitt, Marvin, et al.
Published: (2024)
Novel Pivoted Cholesky Decompositions for Efficient Gaussian Process Inference
by: de Roos, Filip, et al.
Published: (2025)
by: de Roos, Filip, et al.
Published: (2025)
Scalable Multi-Output Gaussian Processes with Stochastic Variational Inference
by: Jiang, Xiaoyu, et al.
Published: (2024)
by: Jiang, Xiaoyu, et al.
Published: (2024)
Turbocharging Gaussian Process Inference with Approximate Sketch-and-Project
by: Rathore, Pratik, et al.
Published: (2025)
by: Rathore, Pratik, et al.
Published: (2025)
Instance-Adaptive Parametrization for Amortized Variational Inference
by: Pollastro, Andrea, et al.
Published: (2026)
by: Pollastro, Andrea, et al.
Published: (2026)
From Shallow Bayesian Neural Networks to Gaussian Processes: General Convergence, Identifiability and Scalable Inference
by: de Araújo, Gracielle Antunes, et al.
Published: (2026)
by: de Araújo, Gracielle Antunes, et al.
Published: (2026)
Variational Gaussian Process Diffusion Processes
by: Verma, Prakhar, et al.
Published: (2023)
by: Verma, Prakhar, et al.
Published: (2023)
Computation-Aware Gaussian Processes: Model Selection And Linear-Time Inference
by: Wenger, Jonathan, et al.
Published: (2024)
by: Wenger, Jonathan, et al.
Published: (2024)
From Mice to Trains: Amortized Bayesian Inference on Graph Data
by: Jedhoff, Svenja, et al.
Published: (2026)
by: Jedhoff, Svenja, et al.
Published: (2026)
ALINE: Joint Amortization for Bayesian Inference and Active Data Acquisition
by: Huang, Daolang, et al.
Published: (2025)
by: Huang, Daolang, et al.
Published: (2025)
ASPIRE: Iterative Amortized Posterior Inference for Bayesian Inverse Problems
by: Orozco, Rafael, et al.
Published: (2024)
by: Orozco, Rafael, et al.
Published: (2024)
Inference of Multiscale Gaussian Graphical Model
by: Sanou, Do Edmond, et al.
Published: (2022)
by: Sanou, Do Edmond, et al.
Published: (2022)
JADAI: Jointly Amortizing Adaptive Design and Bayesian Inference
by: Bracher, Niels, et al.
Published: (2025)
by: Bracher, Niels, et al.
Published: (2025)
Empirical Gaussian Processes
by: Lin, Jihao Andreas, et al.
Published: (2026)
by: Lin, Jihao Andreas, et al.
Published: (2026)
Similar Items
-
Amortized Variational Inference for Deep Gaussian Processes
by: Meng, Qiuxian, et al.
Published: (2024) -
Characterizing the Representational Capacity of Neural Processes
by: Young, Robin
Published: (2026) -
On the Conditioning Consistency Gap in Conditional Neural Processes
by: Young, Robin
Published: (2026) -
Neural Methods for Amortized Inference
by: Zammit-Mangion, Andrew, et al.
Published: (2024) -
Neural Processes Maintain Calibrated Biomass Estimates Across Spatiotemporal Gaps and Disturbance
by: Young, Robin, et al.
Published: (2026)