A Framework for Nonstationary Gaussian Processes with Neural Network Parameters
Fuente:
arXiv
Saved in:
| Main Authors: | James, Zachary, Guinness, Joseph |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Implementation and Analysis of GPU Algorithms for Vecchia Approximation
by: James, Zachary, et al.
Published: (2024)
by: James, Zachary, et al.
Published: (2024)
Predicting Covariate-Driven Spatial Deformation for Nonstationary Gaussian Processes
by: Gu, Minghao, et al.
Published: (2026)
by: Gu, Minghao, et al.
Published: (2026)
Neural Networks Decoded: Targeted and Robust Analysis of Neural Network Decisions via Causal Explanations and Reasoning
by: Diallo, Alec F., et al.
Published: (2024)
by: Diallo, Alec F., et al.
Published: (2024)
PCS Workflow for Veridical Data Science in the Age of AI
by: Rewolinski, Zachary T., et al.
Published: (2025)
by: Rewolinski, Zachary T., et al.
Published: (2025)
Towards Learning and Explaining Indirect Causal Effects in Neural Networks
by: Reddy, Abbavaram Gowtham, et al.
Published: (2023)
by: Reddy, Abbavaram Gowtham, et al.
Published: (2023)
When Graph Neural Network Meets Causality: Opportunities, Methodologies and An Outlook
by: Jiang, Wenzhao, et al.
Published: (2023)
by: Jiang, Wenzhao, et al.
Published: (2023)
Enhancing the Performance of Neural Networks Through Causal Discovery and Integration of Domain Knowledge
by: Zhang, Xiaoge, et al.
Published: (2023)
by: Zhang, Xiaoge, et al.
Published: (2023)
Deep Optimal Experimental Design for Parameter Estimation Problems
by: Siddiqui, Md Shahriar Rahim, et al.
Published: (2024)
by: Siddiqui, Md Shahriar Rahim, et al.
Published: (2024)
Sanity Checks Revisited: An Exploration to Repair the Model Parameter Randomisation Test
by: Hedström, Anna, et al.
Published: (2024)
by: Hedström, Anna, et al.
Published: (2024)
Discriminative classification with generative features: bridging Naive Bayes and logistic regression
by: Terner, Zachary, et al.
Published: (2025)
by: Terner, Zachary, et al.
Published: (2025)
A Causal Framework for Evaluating ICU Discharge Strategies
by: Simha, Sagar Nagaraj, et al.
Published: (2026)
by: Simha, Sagar Nagaraj, et al.
Published: (2026)
Causal Fairness under Unobserved Confounding: A Neural Sensitivity Framework
by: Schröder, Maresa, et al.
Published: (2023)
by: Schröder, Maresa, et al.
Published: (2023)
A General Causal Inference Framework for Cross-Sectional Observational Data
by: Zhao, Yonghe, et al.
Published: (2024)
by: Zhao, Yonghe, et al.
Published: (2024)
Combining Priors with Experience: Confidence Calibration Based on Binomial Process Modeling
by: Dong, Jinzong, et al.
Published: (2024)
by: Dong, Jinzong, et al.
Published: (2024)
ProCause: Generating Counterfactual Outcomes to Evaluate Prescriptive Process Monitoring Methods
by: De Moor, Jakob, et al.
Published: (2025)
by: De Moor, Jakob, et al.
Published: (2025)
Optimizing Feature Selection in Causal Inference: A Three-Stage Computational Framework for Unbiased Estimation
by: Yang, Tianyu, et al.
Published: (2025)
by: Yang, Tianyu, et al.
Published: (2025)
TRIALSCOPE: A Unifying Causal Framework for Scaling Real-World Evidence Generation with Biomedical Language Models
by: González, Javier, et al.
Published: (2023)
by: González, Javier, et al.
Published: (2023)
Data-Augmented Few-Shot Neural Emulator for Computer-Model System Identification
by: Jantre, Sanket, et al.
Published: (2025)
by: Jantre, Sanket, et al.
Published: (2025)
rSDNet: Unified Robust Neural Learning against Label Noise and Adversarial Attacks
by: Jana, Suryasis, et al.
Published: (2026)
by: Jana, Suryasis, et al.
Published: (2026)
KANITE: Kolmogorov-Arnold Networks for ITE estimation
by: Mehendale, Eshan, et al.
Published: (2025)
by: Mehendale, Eshan, et al.
Published: (2025)
Explainability as statistical inference
by: Senetaire, Hugo Henri Joseph, et al.
Published: (2022)
by: Senetaire, Hugo Henri Joseph, et al.
Published: (2022)
Shapley-PC: Constraint-based Causal Structure Learning with a Shapley Inspired Framework
by: Russo, Fabrizio, et al.
Published: (2023)
by: Russo, Fabrizio, et al.
Published: (2023)
Adaptive Learning of the Latent Space of Wasserstein Generative Adversarial Networks
by: Qiu, Yixuan, et al.
Published: (2024)
by: Qiu, Yixuan, et al.
Published: (2024)
New Statistical Framework for Extreme Error Probability in High-Stakes Domains for Reliable Machine Learning
by: Michelucci, Umberto, et al.
Published: (2025)
by: Michelucci, Umberto, et al.
Published: (2025)
Causal and Local Correlations Based Network for Multivariate Time Series Classification
by: Du, Mingsen, et al.
Published: (2024)
by: Du, Mingsen, et al.
Published: (2024)
NeuroMAS: Multi-Agent Systems as Neural Networks with Joint Reinforcement Learning
by: Lu, Haoran, et al.
Published: (2026)
by: Lu, Haoran, et al.
Published: (2026)
Causal Relationship Network of Risk Factors Impacting Workday Loss in Underground Coal Mines
by: Ren, Shangsi, et al.
Published: (2024)
by: Ren, Shangsi, et al.
Published: (2024)
De-confounding Representation Learning for Counterfactual Inference on Continuous Treatment via Generative Adversarial Network
by: Zhao, Yonghe, et al.
Published: (2023)
by: Zhao, Yonghe, et al.
Published: (2023)
M$^3$TN: Multi-gate Mixture-of-Experts based Multi-valued Treatment Network for Uplift Modeling
by: Sun, Zexu, et al.
Published: (2024)
by: Sun, Zexu, et al.
Published: (2024)
Scalable Spatiotemporal Prediction with Bayesian Neural Fields
by: Saad, Feras, et al.
Published: (2024)
by: Saad, Feras, et al.
Published: (2024)
Removing Spurious Correlation from Neural Network Interpretations
by: Fotouhi, Milad, et al.
Published: (2024)
by: Fotouhi, Milad, et al.
Published: (2024)
Discovering and Reasoning of Causality in the Hidden World with Large Language Models
by: Liu, Chenxi, et al.
Published: (2024)
by: Liu, Chenxi, et al.
Published: (2024)
Multi-Band Variable-Lag Granger Causality: A Unified Framework for Causal Time Series Inference across Frequencies
by: Sookkongwaree, Chakattrai, et al.
Published: (2025)
by: Sookkongwaree, Chakattrai, et al.
Published: (2025)
High-dimensional multiple imputation (HDMI) for partially observed confounders including natural language processing-derived auxiliary covariates
by: Weberpals, Janick, et al.
Published: (2024)
by: Weberpals, Janick, et al.
Published: (2024)
Uncertainty Quantification for Prior-Data Fitted Networks using Martingale Posteriors
by: Nagler, Thomas, et al.
Published: (2025)
by: Nagler, Thomas, et al.
Published: (2025)
Bridging the Unavoidable A Priori: A Framework for Comparative Causal Modeling
by: Hovmand, Peter S., et al.
Published: (2025)
by: Hovmand, Peter S., et al.
Published: (2025)
A general framework for adaptive nonparametric dimensionality reduction
by: Di Noia, Antonio, et al.
Published: (2025)
by: Di Noia, Antonio, et al.
Published: (2025)
Is Elo Rating Reliable? A Study Under Model Misspecification
by: Tang, Shange, et al.
Published: (2025)
by: Tang, Shange, et al.
Published: (2025)
A Recipe for Causal Graph Regression: Confounding Effects Revisited
by: Yin, Yujia, et al.
Published: (2025)
by: Yin, Yujia, et al.
Published: (2025)
A Consequentialist Critique of Binary Classification Evaluation: Theory, Practice, and Tools
by: Flores, Gerardo, et al.
Published: (2025)
by: Flores, Gerardo, et al.
Published: (2025)
Similar Items
-
Implementation and Analysis of GPU Algorithms for Vecchia Approximation
by: James, Zachary, et al.
Published: (2024) -
Predicting Covariate-Driven Spatial Deformation for Nonstationary Gaussian Processes
by: Gu, Minghao, et al.
Published: (2026) -
Neural Networks Decoded: Targeted and Robust Analysis of Neural Network Decisions via Causal Explanations and Reasoning
by: Diallo, Alec F., et al.
Published: (2024) -
PCS Workflow for Veridical Data Science in the Age of AI
by: Rewolinski, Zachary T., et al.
Published: (2025) -
Towards Learning and Explaining Indirect Causal Effects in Neural Networks
by: Reddy, Abbavaram Gowtham, et al.
Published: (2023)