Expectation Propagation for approximate Bayesian inference

10 January 2013

Papers citing "Expectation Propagation for approximate Bayesian inference"

50 / 491 papers shown

Title
Moment Matching Denoising Gibbs Sampling Mingtian Zhang Alex Hawkins-Hooker Brooks Paige David Barber DiffM 26 3 0 19 May 2023
Uncertainty Quantification in Machine Learning for Engineering Design and Health Prognostics: A Tutorial V. Nemani Luca Biggio Xun Huan Zhen Hu Olga Fink Anh Tran Yan Wang Xiaoge Zhang Chao Hu AI4CE 33 75 0 07 May 2023
IBIA: An Incremental Build-Infer-Approximate Framework for Approximate Inference of Partition Function Shivani Bathla V. Vasudevan TPM 22 1 0 13 Apr 2023
Inverse Unscented Kalman Filter Himali Singh Kumar Vijay Mishra Arpan Chattopadhyay AAML 17 6 0 04 Apr 2023
Autoregressive Conditional Neural Processes W. Bruinsma Stratis Markou James Requiema Andrew Y. K. Foong Tom R. Andersson Anna Vaughan Anthony Buonomo J. S. Hosking Richard Turner BDL UQCV 30 21 0 25 Mar 2023
Nonlinear Kalman Filtering with Reparametrization Gradients San Gultekin B. Kitts A. Flores John Paisley 9 0 0 08 Mar 2023
Variational EP with Probabilistic Backpropagation for Bayesian Neural Networks Kehinde Olobatuyi BDL 9 0 0 02 Mar 2023
A Targeted Accuracy Diagnostic for Variational Approximations Yu-Xiang Wang Mikolaj Kasprzak Jonathan H. Huggins DRL 25 1 0 24 Feb 2023
Federated Learning as Variational Inference: A Scalable Expectation Propagation Approach Han Guo P. Greengard Hongyi Wang Andrew Gelman Yoon Kim Eric P. Xing FedML 21 20 0 08 Feb 2023
Towards Practical Preferential Bayesian Optimization with Skew Gaussian Processes Shion Takeno Masahiro Nomura Masayuki Karasuyama 27 17 0 03 Feb 2023
QCM-SGM+: Improved Quantized Compressed Sensing With Score-Based Generative Models Xiangming Meng Y. Kabashima 45 1 0 02 Feb 2023
Robust Gaussian Process Regression with Huber Likelihood Pooja Algikar L. Mili GP 14 7 0 19 Jan 2023
Skewed Bernstein-von Mises theorem and skew-modal approximations Daniele Durante Francesco Pozza Botond Szabó 39 12 0 08 Jan 2023
Introducing Variational Inference in Statistics and Data Science Curriculum Vojtech Kejzlar Jingchen Hu 11 3 0 03 Jan 2023
Fundamental Limits of Two-layer Autoencoders, and Achieving Them with Gradient Methods A. Shevchenko Kevin Kögler Hamed Hassani Marco Mondelli DRL MLT 19 2 0 27 Dec 2022
Moment Propagation J. Ormerod Weichang Yu 16 0 0 21 Nov 2022
Personalized Federated Learning with Hidden Information on Personalized Prior Mingjia Shi Yuhao Zhou Qing Ye Jiancheng Lv FedML 29 3 0 19 Nov 2022
Towards Improved Learning in Gaussian Processes: The Best of Two Worlds Rui Li S. T. John Arno Solin BDL GP 17 0 0 11 Nov 2022
Joint Entropy Search for Multi-objective Bayesian Optimization Ben Tu Axel Gandy N. Kantas B. Shafei 21 38 0 06 Oct 2022
GFlowNets and variational inference Nikolay Malkin Salem Lahlou T. Deleu Xu Ji J. E. Hu Katie Everett Dinghuai Zhang Yoshua Bengio BDL 134 77 0 02 Oct 2022
Counter-Adversarial Learning with Inverse Unscented Kalman Filter Himali Singh Kumar Vijay Mishra Arpan Chattopadhyay 19 5 0 01 Oct 2022
Amortized Variational Inference: A Systematic Review Ankush Ganguly Sanjana Jain Ukrit Watchareeruetai 25 14 0 22 Sep 2022
Monotonic Gaussian process for physics-constrained machine learning with materials science applications Anh Tran Kathryn A. Maupin T. Rodgers PINN AI4CE 19 6 0 31 Aug 2022
Image Reconstruction by Splitting Expectation Propagation Techniques from Iterative Inversion R. Aykroyd Kehinde Olobatuyi 11 0 0 25 Aug 2022
The split Gibbs sampler revisited: improvements to its algorithmic structure and augmented target distribution Marcelo Pereyra L. Mieles K. Zygalakis 42 6 0 28 Jun 2022
Distributional Gaussian Processes Layers for Out-of-Distribution Detection S. Popescu D. Sharp James H. Cole Konstantinos Kamnitsas Ben Glocker OOD 26 0 0 27 Jun 2022
Risk-averse Contextual Multi-armed Bandit Problem with Linear Payoffs Yifan Lin Yuhao Wang Enlu Zhou 13 4 0 24 Jun 2022
Capacity Optimality of OAMP in Coded Large Unitarily Invariant Systems Lei Liu Shansuo Liang L. Ping 13 16 0 23 Jun 2022
Bayesian non-conjugate regression via variational message passing C. Castiglione M. Bernardi 21 0 0 19 Jun 2022
Bayesian conjugacy in probit, tobit, multinomial probit and extensions: A review and new results Niccolò Anceschi A. Fasano Daniele Durante Giacomo Zanella 25 16 0 16 Jun 2022
Federated Data Analytics: A Study on Linear Models Xubo Yue Raed Al Kontar Ana María Estrada Gómez FedML 26 12 0 15 Jun 2022
Factored Conditional Filtering: Tracking States and Estimating Parameters in High-Dimensional Spaces Dawei Chen Samuel Yang-Zhao John Lloyd K. S. Ng AI4TS 4 1 0 05 Jun 2022
A Look at Improving Robustness in Visual-inertial SLAM by Moment Matching Arno Solin Ruixiao Li Andrea Pilzer 8 1 0 27 May 2022
Scalable Stochastic Parametric Verification with Stochastic Variational Smoothed Model Checking Luca Bortolussi Francesca Cairoli Ginevra Carbone Paolo Pulcini 34 1 0 11 May 2022
A piece-wise constant approximation for non-conjugate Gaussian Process models Sarem Seitz 12 0 0 22 Apr 2022
Conditional Injective Flows for Bayesian Imaging AmirEhsan Khorashadizadeh K. Kothari Leonardo Salsi Ali Aghababaei Harandi Maarten V. de Hoop Ivan Dokmanić MedIm 26 16 0 15 Apr 2022
Look-Ahead Acquisition Functions for Bernoulli Level Set Estimation Benjamin Letham Phillip Guan Chase Tymms E. Bakshy Michael Shvartsman 32 10 0 18 Mar 2022
Discovering Inductive Bias with Gibbs Priors: A Diagnostic Tool for Approximate Bayesian Inference Luca Rendsburg Agustinus Kristiadi Philipp Hennig U. V. Luxburg 16 2 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 19 12 0 24 Feb 2022
IBIA: Bayesian Inference via Incremental Build-Infer-Approximate operations on Clique Trees Shivani Bathla V. Vasudevan 16 2 0 24 Feb 2022
Efficient CDF Approximations for Normalizing Flows Chandramouli Shama Sastry Andreas M. Lehrmann Marcus A. Brubaker A. Radovic 14 1 0 23 Feb 2022
Analysis of Random Sequential Message Passing Algorithms for Approximate Inference Burak Çakmak Yue M. Lu Manfred Opper 20 3 0 16 Feb 2022
Stochastic Neural Networks with Infinite Width are Deterministic Liu Ziyin Hanlin Zhang Xiangming Meng Yuting Lu Eric P. Xing Masakuni Ueda 29 3 0 30 Jan 2022
Bayesian Nonlinear Models for Repeated Measurement Data: An Overview, Implementation, and Applications Se Yoon Lee 19 18 0 28 Jan 2022
Analytic-DPM: an Analytic Estimate of the Optimal Reverse Variance in Diffusion Probabilistic Models Fan Bao Chongxuan Li Jun Zhu Bo Zhang DiffM 54 337 0 17 Jan 2022
Loss-calibrated expectation propagation for approximate Bayesian decision-making Michael J. Morais Jonathan W. Pillow 44 6 0 10 Jan 2022
Inverse Extended Kalman Filter -- Part I: Fundamentals Himali Singh Arpan Chattopadhyay Kumar Vijay Mishra 20 13 0 05 Jan 2022
Variational Learning for the Inverted Beta-Liouville Mixture Model and Its Application to Text Categorization Yongfa Ling Wenbo Guan Qiang Ruan Heping Song Yuping Lai BDL 11 5 0 29 Dec 2021
Challenges and Opportunities in Approximate Bayesian Deep Learning for Intelligent IoT Systems Meet P. Vadera Benjamin M. Marlin UQCV BDL 18 5 0 03 Dec 2021
An adaptive mixture-population Monte Carlo method for likelihood-free inference Zhijian He Shifeng Huo Tianhui Yang 6 2 0 01 Dec 2021