Cover Image

FSP-Laplace: Function-Space Priors for the Laplace Approximation in Bayesian Deep Learning

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Cinquin, Tristan, Pförtner, Marvin, Fortuin, Vincent, Hennig, Philipp, Bamler, Robert
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

laplax -- Laplace Approximations with JAX
by: Weber, Tobias, et al.
Published: (2025)

Regularized KL-Divergence for Well-Defined Function-Space Variational Inference in Bayesian neural networks
by: Cinquin, Tristan, et al.
Published: (2024)

Tubular Riemannian Laplace Approximations for Bayesian Neural Networks
by: David, Rodrigo Pereira
Published: (2025)

Self-Supervised Laplace Approximation for Bayesian Uncertainty Quantification
by: Rodemann, Julian, et al.
Published: (2026)

Limits of PRM-Guided Tree Search for Mathematical Reasoning with LLMs
by: Cinquin, Tristan, et al.
Published: (2025)

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

Scaling up Probabilistic PDE Simulators with Structured Volumetric Information
by: Weiland, Tim, et al.
Published: (2024)

Flexible and Efficient Probabilistic PDE Solvers through Gaussian Markov Random Fields
by: Weiland, Tim, et al.
Published: (2025)

A Compact Representation for Bayesian Neural Networks By Removing Permutation Symmetry
by: Xiao, Tim Z., et al.
Published: (2023)

Incorporating Unlabelled Data into Bayesian Neural Networks
by: Sharma, Mrinank, et al.
Published: (2023)

Improving Group Fairness in Knowledge Distillation via Laplace Approximation of Early Exits
by: Fasth, Edvin, et al.
Published: (2025)

LAPLEX: The FFT of Learnable Laplace Kernels
by: Struski, Łukasz, et al.
Published: (2026)

Variational Linearized Laplace Approximation for Bayesian Deep Learning
by: Ortega, Luis A., et al.
Published: (2023)

Bayesian Deep Learning Via Expectation Maximization and Turbo Deep Approximate Message Passing
by: Xu, Wei, et al.
Published: (2024)

Laplace Sample Information: Data Informativeness Through a Bayesian Lens
by: Kaiser, Johannes, et al.
Published: (2025)

Identity Curvature Laplace Approximation for Improved Out-of-Distribution Detection
by: Zhdanov, Maksim, et al.
Published: (2023)

Latent Laplace Diffusion for Irregular Multivariate Time Series
by: You, Zinuo, et al.
Published: (2026)

Neural Laplace for learning Stochastic Differential Equations
by: Carrel, Adrien
Published: (2024)

Computation-Aware Kalman Filtering and Smoothing
by: Pförtner, Marvin, et al.
Published: (2024)

Physics-Informed Gaussian Process Regression Generalizes Linear PDE Solvers
by: Pförtner, Marvin, et al.
Published: (2022)

Linearization Turns Neural Operators into Function-Valued Gaussian Processes
by: Magnani, Emilia, et al.
Published: (2024)

Towards Dynamic Feature Acquisition on Medical Time Series by Maximizing Conditional Mutual Information
by: Sergeev, Fedor, et al.
Published: (2024)

Addressing the Inconsistency in Bayesian Deep Learning via Generalized Laplace Approximation
by: Chen, Yinsong, et al.
Published: (2024)

Efficient Bayesian Updates for Deep Active Learning via Laplace Approximations
by: Huseljic, Denis, et al.
Published: (2022)

From Markov to Laplace: How Mamba In-Context Learns Markov Chains
by: Bondaschi, Marco, et al.
Published: (2025)

Priors Matter: Addressing Misspecification in Bayesian Deep Q-Learning
by: van der Vaart, Pascal R., et al.
Published: (2025)

In-Context Reinforcement Learning through Bayesian Fusion of Context and Value Prior
by: Berkes, Anaïs, et al.
Published: (2026)

FLDmamba: Integrating Fourier and Laplace Transform Decomposition with Mamba for Enhanced Time Series Prediction
by: Zhang, Qianru, et al.
Published: (2025)

Amortising Inference and Meta-Learning Priors in Neural Networks
by: Rochussen, Tommy, et al.
Published: (2026)

Towards Leveraging AutoML for Sustainable Deep Learning: A Multi-Objective HPO Approach on Deep Shift Neural Networks
by: Hennig, Leona, et al.
Published: (2024)

Reparameterization invariance in approximate Bayesian inference
by: Roy, Hrittik, et al.
Published: (2024)

Sample Path Regularity of Gaussian Processes from the Covariance Kernel
by: Da Costa, Nathaël, et al.
Published: (2023)

Laplace Approximation for Bayesian Tensor Network Kernel Machines
by: Saiapin, Albert, et al.
Published: (2026)

On Evaluating LLMs' Capabilities as Functional Approximators: A Bayesian Perspective
by: Siddiqui, Shoaib Ahmed, et al.
Published: (2024)

Laplace Learning in Wasserstein Space
by: Oliver, Mary Chriselda Antony, et al.
Published: (2025)

Algebraic Priors for Approximately Equivariant Networks
by: Ali, Riccardo, et al.
Published: (2025)

Simulation Priors for Data-Efficient Deep Learning
by: Treven, Lenart, et al.
Published: (2025)

Collapsed Inference for Bayesian Deep Learning
by: Zeng, Zhe, et al.
Published: (2023)

Your Finetuned Large Language Model is Already a Powerful Out-of-distribution Detector
by: Zhang, Andi, et al.
Published: (2024)

Bayesian Concept Bottleneck Models with LLM Priors
by: Feng, Jean, et al.
Published: (2024)