The Bayesian Learning Rule

9 July 2021

Håvard Rue

Papers citing "The Bayesian Learning Rule"

50 / 53 papers shown

Title
Variational Formulation of the Particle Flow Particle Filter Yinzhuang Yi Jorge Cortés Nikolay A. Atanasov 22 0 0 06 May 2025
AutoBayes: A Compositional Framework for Generalized Variational Inference Toby St Clere Smithe Marco Perin BDL CoGe 69 0 0 24 Mar 2025
Uncertainty-Aware Decoding with Minimum Bayes Risk Nico Daheim Clara Meister Thomas Möllenhoff Iryna Gurevych 53 0 0 07 Mar 2025
Variational Bayesian Pseudo-Coreset Hyungi Lee S. Lee Juho Lee BDL 36 0 0 28 Feb 2025
Spectral-factorized Positive-definite Curvature Learning for NN Training Wu Lin Felix Dangel Runa Eschenhagen Juhan Bae Richard E. Turner Roger B. Grosse 45 0 0 10 Feb 2025
Hellinger-Kantorovich Gradient Flows: Global Exponential Decay of Entropy Functionals Alexander Mielke Jia Jie Zhu 56 1 0 28 Jan 2025
Investigating Plausibility of Biologically Inspired Bayesian Learning in ANNs Ram Zaveri CLL 61 0 0 27 Nov 2024
Personalizing Low-Rank Bayesian Neural Networks Via Federated Learning Boning Zhang Dongzhu Liu Osvaldo Simeone Guanchu Wang Dimitrios Pezaros Guangxu Zhu BDL FedML 26 0 0 18 Oct 2024
Bayesian Online Natural Gradient (BONG) Matt Jones Peter Chang Kevin P. Murphy BDL 45 3 0 30 May 2024
Evaluating Bayesian deep learning for radio galaxy classification Devina Mohan Anna M. M. Scaife UQCV BDL 31 1 0 28 May 2024
Outlier-robust Kalman Filtering through Generalised Bayes Gerardo Duran-Martín Matias Altamirano Alexander Y. Shestopaloff Leandro Sánchez-Betancourt Jeremias Knoblauch Matt Jones F. Briol Kevin P. Murphy 50 8 0 09 May 2024
eXponential FAmily Dynamical Systems (XFADS): Large-scale nonlinear Gaussian state-space modeling Matthew Dowling Yuan Zhao Il Memming Park BDL 25 5 0 03 Mar 2024
Variational Learning is Effective for Large Deep Networks Yuesong Shen Nico Daheim Bai Cong Peter Nickl Gian Maria Marconi ... Rio Yokota Iryna Gurevych Daniel Cremers Mohammad Emtiyaz Khan Thomas Möllenhoff 30 21 0 27 Feb 2024
Can We Remove the Square-Root in Adaptive Gradient Methods? A Second-Order Perspective Wu Lin Felix Dangel Runa Eschenhagen Juhan Bae Richard E. Turner Alireza Makhzani ODL 44 12 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
Structured Inverse-Free Natural Gradient: Memory-Efficient & Numerically-Stable KFAC Wu Lin Felix Dangel Runa Eschenhagen Kirill Neklyudov Agustinus Kristiadi Richard E. Turner Alireza Makhzani 6 3 0 09 Dec 2023
Efficient Neural Networks for Tiny Machine Learning: A Comprehensive Review M. Lê Pierre Wolinski Julyan Arbel 27 8 0 20 Nov 2023
Implicit Maximum a Posteriori Filtering via Adaptive Optimization Gianluca M Bencomo Jake C. Snell Thomas L. Griffiths 51 3 0 17 Nov 2023
The Memory Perturbation Equation: Understanding Model's Sensitivity to Data Peter Nickl Lu Xu Dharmesh Tailor Thomas Möllenhoff Mohammad Emtiyaz Khan 11 10 0 30 Oct 2023
Probabilistic Reach-Avoid for Bayesian Neural Networks Matthew Wicker Luca Laurenti A. Patané Nicola Paoletti Alessandro Abate Marta Z. Kwiatkowska 16 2 0 03 Oct 2023
The Interpolating Information Criterion for Overparameterized Models Liam Hodgkinson Christopher van der Heide Roberto Salomone Fred Roosta Michael W. Mahoney 11 7 0 15 Jul 2023
G-TRACER: Expected Sharpness Optimization John R. Williams Stephen J. Roberts 11 0 0 24 Jun 2023
Beyond Deep Ensembles: A Large-Scale Evaluation of Bayesian Deep Learning under Distribution Shift Florian Seligmann P. Becker Michael Volpp Gerhard Neumann UQCV 14 14 0 21 Jun 2023
Memory-Based Dual Gaussian Processes for Sequential Learning Paul E. Chang Prakhar Verma S. T. John Arno Solin Mohammad Emtiyaz Khan GP 12 4 0 06 Jun 2023
A Study of Bayesian Neural Network Surrogates for Bayesian Optimization Y. Li Tim G. J. Rudner A. Wilson BDL 19 27 0 31 May 2023
Variational Bayes Made Easy Mohammad Emtiyaz Khan BDL 11 1 0 27 Apr 2023
The Lie-Group Bayesian Learning Rule E. M. Kıral Thomas Möllenhoff Mohammad Emtiyaz Khan BDL 10 2 0 08 Mar 2023
Bayesian Inference on Binary Spiking Networks Leveraging Nanoscale Device Stochasticity Prabodh Katti N. Skatchkovsky Osvaldo Simeone Bipin Rajendran Bashir M. Al-Hashimi 6 4 0 02 Feb 2023
AskewSGD : An Annealed interval-constrained Optimisation method to train Quantized Neural Networks Louis Leconte S. Schechtman Eric Moulines 19 4 0 07 Nov 2022
Fantasizing with Dual GPs in Bayesian Optimization and Active Learning Paul E. Chang Prakhar Verma S. T. John Victor Picheny Henry B. Moss Arno Solin GP 17 6 0 02 Nov 2022
Inferring Smooth Control: Monte Carlo Posterior Policy Iteration with Gaussian Processes Joe Watson Jan Peters 16 15 0 07 Oct 2022
SAM as an Optimal Relaxation of Bayes Thomas Möllenhoff Mohammad Emtiyaz Khan BDL 16 32 0 04 Oct 2022
Compressed Particle-Based Federated Bayesian Learning and Unlearning J. Gong Osvaldo Simeone Joonhyuk Kang FedML 44 10 0 14 Sep 2022
Conjugate Natural Selection Reilly P. Raab Luca de Alfaro Yang Liu 10 0 0 29 Aug 2022
Bayesian Continual Learning via Spiking Neural Networks N. Skatchkovsky Hyeryung Jang Osvaldo Simeone BDL 17 17 0 29 Aug 2022
Robustness to corruption in pre-trained Bayesian neural networks Xi Wang Laurence Aitchison OOD UQCV 9 4 0 24 Jun 2022
Variational inference via Wasserstein gradient flows Marc Lambert Sinho Chewi Francis R. Bach Silvère Bonnabel Philippe Rigollet BDL DRL 14 67 0 31 May 2022
Quasi Black-Box Variational Inference with Natural Gradients for Bayesian Learning M. Magris M. Shabani Alexandros Iosifidis BDL 23 4 0 23 May 2022
Bayesian Learning Approach to Model Predictive Control Namhoon Cho Seokwon Lee Hyo-Sang Shin Antonios Tsourdos 8 1 0 05 Mar 2022
Efficient Natural Gradient Descent Methods for Large-Scale PDE-Based Optimization Problems L. Nurbekyan Wanzhou Lei Yunbo Yang 8 12 0 13 Feb 2022
AdaSTE: An Adaptive Straight-Through Estimator to Train Binary Neural Networks Huu Le R. Høier Che-Tsung Lin Christopher Zach 40 15 0 06 Dec 2021
Kalman filters as the steady-state solution of gradient descent on variational free energy M. Baltieri Takuya Isomura 6 6 0 20 Nov 2021
Bayesian Learning via Neural Schrödinger-Föllmer Flows Francisco Vargas Andrius Ovsianas David Fernandes Mark Girolami Neil D. Lawrence Nikolas Nusken BDL 22 44 0 20 Nov 2021
Bayes-Newton Methods for Approximate Bayesian Inference with PSD Guarantees William J. Wilkinson Simo Särkkä Arno Solin BDL 13 15 0 02 Nov 2021
User-friendly introduction to PAC-Bayes bounds Pierre Alquier FedML 28 196 0 21 Oct 2021
A Survey of Learning Criteria Going Beyond the Usual Risk Matthew J. Holland Kazuki Tanabe FaML 17 4 0 11 Oct 2021
Tensor Normal Training for Deep Learning Models Yi Ren D. Goldfarb 11 26 0 05 Jun 2021
Connections and Equivalences between the Nyström Method and Sparse Variational Gaussian Processes Veit Wild Motonobu Kanagawa Dino Sejdinovic 17 16 0 02 Jun 2021
Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam Mohammad Emtiyaz Khan Didrik Nielsen Voot Tangkaratt Wu Lin Y. Gal Akash Srivastava ODL 74 264 0 13 Jun 2018
On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima N. Keskar Dheevatsa Mudigere J. Nocedal M. Smelyanskiy P. T. P. Tang ODL 273 2,878 0 15 Sep 2016