Sampling-based inference for large linear models, with application to
linearised Laplace

Sampling-based inference for large linear models, with application to linearised Laplace

10 October 2022

Javier Antorán

Riccardo Barbano

Eric T. Nalisnick

José Miguel Hernández-Lobato

Papers citing "Sampling-based inference for large linear models, with application to linearised Laplace"

17 / 17 papers shown

Title
Bayes without Underfitting: Fully Correlated Deep Learning Posteriors via Alternating Projections M. Miani Hrittik Roy Søren Hauberg UQCV BDL 32 0 0 22 Oct 2024
Reparameterization invariance in approximate Bayesian inference Hrittik Roy M. Miani Carl Henrik Ek Philipp Hennig Marvin Pfortner Lukas Tatzel Søren Hauberg BDL 42 8 0 05 Jun 2024
Improving Linear System Solvers for Hyperparameter Optimisation in Iterative Gaussian Processes J. Lin Shreyas Padhy Bruno Mlodozeniec Javier Antorán José Miguel Hernández-Lobato 22 2 0 28 May 2024
Warm Start Marginal Likelihood Optimisation for Iterative Gaussian Processes J. Lin Shreyas Padhy Bruno Mlodozeniec José Miguel Hernández-Lobato 17 1 0 28 May 2024
Partially Stochastic Infinitely Deep Bayesian Neural Networks Sergio Calvo-Ordoñez Matthieu Meunier Francesco Piatti Yuantao Shi BDL 37 3 0 05 Feb 2024
Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI Theodore Papamarkou Maria Skoularidou Konstantina Palla Laurence Aitchison Julyan Arbel ... David Rügamer Yee Whye Teh Max Welling Andrew Gordon Wilson Ruqi Zhang UQCV BDL 35 27 0 01 Feb 2024
Exploration via linearly perturbed loss minimisation David Janz Shuai Liu Alex Ayoub Csaba Szepesvári 11 6 0 13 Nov 2023
Stochastic Gradient Descent for Gaussian Processes Done Right J. Lin Shreyas Padhy Javier Antorán Austin Tripp Alexander Terenin Csaba Szepesvári José Miguel Hernández-Lobato David Janz 9 7 0 31 Oct 2023
Online Laplace Model Selection Revisited J. Lin Javier Antorán José Miguel Hernández-Lobato BDL 22 3 0 12 Jul 2023
Sampling from Gaussian Process Posteriors using Stochastic Gradient Descent J. Lin Javier Antorán Shreyas Padhy David Janz José Miguel Hernández-Lobato Alexander Terenin 16 22 0 20 Jun 2023
Stochastic Marginal Likelihood Gradients using Neural Tangent Kernels Alexander Immer Tycho F. A. van der Ouderaa Mark van der Wilk Gunnar Rätsch Bernhard Schölkopf BDL 25 11 0 06 Jun 2023
Promises and Pitfalls of the Linearized Laplace in Bayesian Optimization Agustinus Kristiadi Alexander Immer Runa Eschenhagen Vincent Fortuin BDL UQCV 13 8 0 17 Apr 2023
Variational Linearized Laplace Approximation for Bayesian Deep Learning Luis A. Ortega Simón Rodríguez Santana Daniel Hernández-Lobato BDL UQCV 28 4 0 24 Feb 2023
Accelerated Linearized Laplace Approximation for Bayesian Deep Learning Zhijie Deng Feng Zhou Jun Zhu BDL 42 19 0 23 Oct 2022
Uncertainty Estimation for Computed Tomography with a Linearised Deep Image Prior Javier Antorán Riccardo Barbano Johannes Leuschner José Miguel Hernández-Lobato Bangti Jin UQCV 22 10 0 28 Feb 2022
Anti-Concentrated Confidence Bonuses for Scalable Exploration Jordan T. Ash Cyril Zhang Surbhi Goel A. Krishnamurthy Sham Kakade 15 6 0 21 Oct 2021
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles Balaji Lakshminarayanan Alexander Pritzel Charles Blundell UQCV BDL 268 5,652 0 05 Dec 2016