Cover Image

Gaussian Process Neural Additive Models

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Zhang, Wei, Barr, Brian, Paisley, John
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

An Explainable Gaussian Process Auto-encoder for Tabular Data
by: Zhang, Wei, et al.
Published: (2025)

Gaussian Process Tilted Nonparametric Density Estimation using Fisher Divergence Score Matching
by: Paisley, John, et al.
Published: (2025)

Sparse Inducing Points in Deep Gaussian Processes: Enhancing Modeling with Denoising Diffusion Variational Inference
by: Xu, Jian, et al.
Published: (2024)

Neural Bridge Processes
by: Xu, Jian, et al.
Published: (2025)

Sparse Variational Student-t Processes for Heavy-tailed Modeling
by: Xu, Jian, et al.
Published: (2024)

Variational Learning of Gaussian Process Latent Variable Models through Stochastic Gradient Annealed Importance Sampling
by: Xu, Jian, et al.
Published: (2024)

Fully Bayesian Differential Gaussian Processes through Stochastic Differential Equations
by: Xu, Jian, et al.
Published: (2024)

Tabular Diffusion Counterfactual Explanations
by: Zhang, Wei, et al.
Published: (2025)

Neural Additive Experts: Context-Gated Experts for Controllable Model Additivity
by: Xiong, Guangzhi, et al.
Published: (2026)

Stochastic Variational Inference with Tuneable Stochastic Annealing
by: Paisley, John, et al.
Published: (2025)

Flexible Bayesian Last Layer Models Using Implicit Priors and Diffusion Posterior Sampling
by: Xu, Jian, et al.
Published: (2024)

A Minimum Variance Path Principle for Accurate and Stable Score-Based Density Ratio Estimation
by: Chen, Wei, et al.
Published: (2026)

ProtoNAM: Prototypical Neural Additive Models for Interpretable Deep Tabular Learning
by: Xiong, Guangzhi, et al.
Published: (2024)

Towards Disentangled Preference Optimization Dynamics: Suppress the Loser, Preserve the Winner
by: Chen, Wei, et al.
Published: (2026)

The Interpretable and Effective Graph Neural Additive Networks
by: Bechler-Speicher, Maya, et al.
Published: (2024)

A Framework for Nonstationary Gaussian Processes with Neural Network Parameters
by: James, Zachary, et al.
Published: (2025)

On the Laplace Approximation as Model Selection Criterion for Gaussian Processes
by: Besginow, Andreas, et al.
Published: (2024)

From Shallow Bayesian Neural Networks to Gaussian Processes: General Convergence, Identifiability and Scalable Inference
by: de Araújo, Gracielle Antunes, et al.
Published: (2026)

Tensor Polynomial Additive Model
by: Chen, Yang, et al.
Published: (2024)

Dynamic 3D Gaussian Tracking for Graph-Based Neural Dynamics Modeling
by: Zhang, Mingtong, et al.
Published: (2024)

Flow-Induced Diagonal Gaussian Processes
by: Lin, Moule, et al.
Published: (2025)

A Unified Gaussian Process for Branching and Nested Hyperparameter Optimization
by: Zhang, Jiazhao, et al.
Published: (2024)

Meta Additive Model: Interpretable Sparse Learning With Auto Weighting
by: Zhang, Xuelin, et al.
Published: (2026)

Transformer Neural Processes - Kernel Regression
by: Jenson, Daniel, et al.
Published: (2024)

Bayesian Analysis of Combinatorial Gaussian Process Bandits
by: Sandberg, Jack, et al.
Published: (2023)

Rényi Neural Processes
by: Wang, Xuesong, et al.
Published: (2024)

Safe Reinforcement Learning via Recovery-based Shielding with Gaussian Process Dynamics Models
by: Goodall, Alexander W., et al.
Published: (2026)

Generating Piano Practice Policy with a Gaussian Process
by: Moringen, Alexandra, et al.
Published: (2024)

Informed Spectral Normalized Gaussian Processes for Trajectory Prediction
by: Schlauch, Christian, et al.
Published: (2024)

Revisiting Kernel Attention with Correlated Gaussian Process Representation
by: Bui, Long Minh, et al.
Published: (2025)

Spectral Transformer Neural Processes
by: Chen, Xianhe, et al.
Published: (2026)

Distance-informed Neural Processes
by: Venkataramanan, Aishwarya, et al.
Published: (2025)

Discrete-Choice Model with Generalized Additive Utility Network
by: Nishi, Tomoki, et al.
Published: (2023)

Diffusion Bridge Variational Inference for Deep Gaussian Processes
by: Xu, Jian, et al.
Published: (2025)

Onsager-Machlup Posterior Transport for Deep Gaussian Processes
by: Xu, Jian, et al.
Published: (2026)

S2MAM: Semi-supervised Meta Additive Model for Robust Estimation and Variable Selection
by: Zhang, Xuelin, et al.
Published: (2026)

Tailored Architectures for Time Series Forecasting: Evaluating Deep Learning Models on Gaussian Process-Generated Data
by: Hankemeier, Victoria, et al.
Published: (2025)

Whom to Trust? Elective Learning for Distributed Gaussian Process Regression
by: Yang, Zewen, et al.
Published: (2024)

Exact Shapley Attributions in Quadratic-time for FANOVA Gaussian Processes
by: Mohammadi, Majid, et al.
Published: (2025)

Robust Satisficing Gaussian Process Bandits Under Adversarial Attacks
by: Saday, Artun, et al.
Published: (2025)