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Conjugate-Computation Variational Inference : Converting Variational Inference in Non-Conjugate Models to Inferences in Conjugate Models

13 March 2017

Mohammad Emtiyaz Khan

Wu Lin

BDL

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Papers citing "Conjugate-Computation Variational Inference : Converting Variational Inference in Non-Conjugate Models to Inferences in Conjugate Models"

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Federated ADMM from Bayesian Duality Thomas Möllenhoff S. Swaroop Finale Doshi-Velez Mohammad Emtiyaz Khan FedML 37 1 0 16 Jun 2025
AutoBayes: A Compositional Framework for Generalized Variational Inference Toby St Clere Smithe Marco Perin BDL CoGe 102 0 0 24 Mar 2025
Diffusion-aware Censored Gaussian Processes for Demand Modelling Filipe Rodrigues DiffM 101 0 0 21 Jan 2025
Investigating Plausibility of Biologically Inspired Bayesian Learning in ANNs Ram Zaveri CLL 126 0 0 27 Nov 2024
Federated Learning with Uncertainty and Personalization via Efficient Second-order Optimization Shivam Pal Aishwarya Gupta Saqib Sarwar Piyush Rai FedML 129 0 0 27 Nov 2024
Approximated Orthogonal Projection Unit: Stabilizing Regression Network Training Using Natural Gradient Shaoqi Wang Chunjie Yang Siwei Lou 42 1 0 23 Sep 2024
Physics-Informed Variational State-Space Gaussian Processes Oliver Hamelijnck Arno Solin Theodoros Damoulas 72 1 0 20 Sep 2024
Efficient, Multimodal, and Derivative-Free Bayesian Inference With Fisher-Rao Gradient Flows Yifan Chen Daniel Zhengyu Huang Jiaoyang Huang Sebastian Reich Andrew M. Stuart 74 7 0 25 Jun 2024
Understanding Stochastic Natural Gradient Variational Inference Kaiwen Wu Jacob R. Gardner BDL 87 2 0 04 Jun 2024
Fearless Stochasticity in Expectation Propagation Jonathan So Richard Turner 66 0 0 03 Jun 2024
Bayesian Online Natural Gradient (BONG) Matt Jones Peter Chang Kevin P. Murphy BDL 75 7 0 30 May 2024
Accelerating Convergence in Bayesian Few-Shot Classification Tianjun Ke Haoqun Cao Feng Zhou 101 0 0 02 May 2024
Function-space Parameterization of Neural Networks for Sequential Learning Aidan Scannell Riccardo Mereu Paul E. Chang Ella Tamir Joni Pajarinen Arno Solin BDL 92 5 0 16 Mar 2024
eXponential FAmily Dynamical Systems (XFADS): Large-scale nonlinear Gaussian state-space modeling Matthew Dowling Yuan Zhao Il Memming Park BDL 62 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 91 31 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 Turner Alireza Makhzani ODL 161 13 0 05 Feb 2024
Joint State Estimation and Noise Identification Based on Variational Optimization Hua Lan Shijie Zhao Jinjie Hu Zengfu Wang Jing-Zhi Fu 41 2 0 15 Dec 2023
Structured Inverse-Free Natural Gradient: Memory-Efficient & Numerically-Stable KFAC Wu Lin Felix Dangel Runa Eschenhagen Kirill Neklyudov Agustinus Kristiadi Richard Turner Alireza Makhzani 65 4 0 09 Dec 2023
The Memory Perturbation Equation: Understanding Model's Sensitivity to Data Peter Nickl Lu Xu Dharmesh Tailor Thomas Möllenhoff Mohammad Emtiyaz Khan 72 11 0 30 Oct 2023
Optimising Distributions with Natural Gradient Surrogates Jonathan So Richard Turner 36 1 0 18 Oct 2023
On variational inference and maximum likelihood estimation with the λ-exponential family Thomas Guilmeau Émilie Chouzenoux Victor Elvira 48 2 0 06 Oct 2023
Sampling via Gradient Flows in the Space of Probability Measures Yifan Chen Daniel Zhengyu Huang Jiaoyang Huang Sebastian Reich Andrew M. Stuart 75 15 0 05 Oct 2023
Improvements on Scalable Stochastic Bayesian Inference Methods for Multivariate Hawkes Process Alex Ziyu Jiang Abel Rodríguez 54 1 0 26 Sep 2023
Improving Hyperparameter Learning under Approximate Inference in Gaussian Process Models Rui Li S. T. John Arno Solin BDL 56 3 0 07 Jun 2023
Variational Gaussian Process Diffusion Processes Prakhar Verma Vincent Adam Arno Solin DiffM 133 6 0 03 Jun 2023
Linear Time GPs for Inferring Latent Trajectories from Neural Spike Trains Matthew Dowling Yuan Zhao Il Memming Park 72 6 0 01 Jun 2023
Revisiting Structured Variational Autoencoders Yixiu Zhao Scott W. Linderman BDL DRL 53 9 0 25 May 2023
On the Convergence of Black-Box Variational Inference Kyurae Kim Jisu Oh Kaiwen Wu Yi-An Ma Jacob R. Gardner BDL 94 17 0 24 May 2023
Real-Time Variational Method for Learning Neural Trajectory and its Dynamics Matthew Dowling Yuan Zhao Il Memming Park BDL OffRL 74 6 0 18 May 2023
Variational Bayes Made Easy Mohammad Emtiyaz Khan BDL 51 1 0 27 Apr 2023
The Lie-Group Bayesian Learning Rule E. M. Kıral Thomas Möllenhoff Mohammad Emtiyaz Khan BDL 52 3 0 08 Mar 2023
Gradient Flows for Sampling: Mean-Field Models, Gaussian Approximations and Affine Invariance Yifan Chen Daniel Zhengyu Huang Jiaoyang Huang Sebastian Reich Andrew M. Stuart 80 19 0 21 Feb 2023
Simplifying Momentum-based Positive-definite Submanifold Optimization with Applications to Deep Learning Wu Lin Valentin Duruisseaux Melvin Leok Frank Nielsen Mohammad Emtiyaz Khan Mark Schmidt 104 10 0 20 Feb 2023
Short-term Prediction and Filtering of Solar Power Using State-Space Gaussian Processes Sean Nassimiha Peter Dudfield Jack Kelly M. Deisenroth So Takao 40 1 0 01 Feb 2023
Towards Improved Learning in Gaussian Processes: The Best of Two Worlds Marcus Klasson S. T. John Arno Solin BDL GP 46 0 0 11 Nov 2022
Regularized Rényi divergence minimization through Bregman proximal gradient algorithms Thomas Guilmeau Émilie Chouzenoux Victor Elvira 86 3 0 09 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 96 6 0 02 Nov 2022
Manifold Gaussian Variational Bayes on the Precision Matrix M. Magris M. Shabani Alexandros Iosifidis 68 2 0 26 Oct 2022
Differentially private partitioned variational inference Mikko A. Heikkilä Matthew Ashman S. Swaroop Richard Turner Antti Honkela FedML 61 2 0 23 Sep 2022
A Unified Perspective on Natural Gradient Variational Inference with Gaussian Mixture Models Oleg Arenz Philipp Dahlinger Zihan Ye Michael Volpp Gerhard Neumann 126 17 0 23 Sep 2022
Bayesian Continual Learning via Spiking Neural Networks N. Skatchkovsky Hyeryung Jang Osvaldo Simeone BDL 73 18 0 29 Aug 2022
Markovian Gaussian Process Variational Autoencoders Harrison Zhu Carles Balsells Rodas Yingzhen Li BDL AI4TS 111 17 0 12 Jul 2022
Quasi Black-Box Variational Inference with Natural Gradients for Bayesian Learning M. Magris M. Shabani Alexandros Iosifidis BDL 75 4 0 23 May 2022
Bayesian Bilinear Neural Network for Predicting the Mid-price Dynamics in Limit-Order Book Markets M. Magris M. Shabani Alexandros Iosifidis 74 10 0 07 Mar 2022
Partitioned Variational Inference: A Framework for Probabilistic Federated Learning Matthew Ashman T. Bui Cuong V Nguyen Efstratios Markou Adrian Weller S. Swaroop Richard Turner FedML 65 14 0 24 Feb 2022
BaLeNAS: Differentiable Architecture Search via the Bayesian Learning Rule Miao Zhang Jilin Hu Steven W. Su Shirui Pan Xiaojun Chang B. Yang Gholamreza Haffari OOD 110 15 0 25 Nov 2021
Dual Parameterization of Sparse Variational Gaussian Processes Vincent Adam Paul E. Chang Mohammad Emtiyaz Khan Arno Solin 89 23 0 05 Nov 2021
Spatio-Temporal Variational Gaussian Processes Oliver Hamelijnck William J. Wilkinson Niki A. Loppi Arno Solin Theodoros Damoulas AI4TS 80 35 0 02 Nov 2021
Bayes-Newton Methods for Approximate Bayesian Inference with PSD Guarantees William J. Wilkinson Simo Särkkä Arno Solin BDL 98 16 0 02 Nov 2021
Conditioning Sparse Variational Gaussian Processes for Online Decision-making Wesley J. Maddox Samuel Stanton A. Wilson 79 32 0 28 Oct 2021