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Variational Inference: A Review for Statisticians

4 January 2016

Papers citing "Variational Inference: A Review for Statisticians"

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Title
NF-ULA: Langevin Monte Carlo with Normalizing Flow Prior for Imaging Inverse Problems Ziruo Cai Junqi Tang Subhadip Mukherjee Jinglai Li Carola Bibiane Schönlieb Xiaoqun Zhang AI4CE 69 4 0 17 Apr 2023
Dimensionality Reduction as Probabilistic Inference Aditya Ravuri Francisco Vargas V. Lalchand Neil D. Lawrence BDL 63 3 0 15 Apr 2023
Bayesian Inference on Brain-Computer Interfaces via GLASS Bangyao Zhao Jane E. Huggins Jian Kang 24 0 0 14 Apr 2023
Black Box Variational Inference with a Deterministic Objective: Faster, More Accurate, and Even More Black Box Ryan Giordano Martin Ingram Tamara Broderick 128 13 0 11 Apr 2023
Forward-backward Gaussian variational inference via JKO in the Bures-Wasserstein Space Michael Diao Krishnakumar Balasubramanian Sinho Chewi Adil Salim BDL 68 29 0 10 Apr 2023
PriorCVAE: scalable MCMC parameter inference with Bayesian deep generative modelling Elizaveta Semenova Prakhar Verma Max Cairney-Leeming Arno Solin Samir Bhatt Seth Flaxman BDL 84 4 0 09 Apr 2023
Bayesian neural networks via MCMC: a Python-based tutorial Rohitash Chandra Royce Chen Joshua Simmons BDL 122 11 0 02 Apr 2023
What Does the Indian Parliament Discuss? An Exploratory Analysis of the Question Hour in the Lok Sabha Suman Adhya Debarshi Kumar Sanyal 114 4 0 01 Apr 2023
Fast inference of latent space dynamics in huge relational event networks I. Artico Ernst C. Wit BDL 101 1 0 29 Mar 2023
Your Diffusion Model is Secretly a Zero-Shot Classifier Alexander C. Li Mihir Prabhudesai Shivam Duggal Ellis L Brown Deepak Pathak DiffM VLM 179 240 0 28 Mar 2023
Efficient Alternating Minimization Solvers for Wyner Multi-View Unsupervised Learning Tengfei Huang H. El Gamal 32 2 0 28 Mar 2023
The Wyner Variational Autoencoder for Unsupervised Multi-Layer Wireless Fingerprinting Tengfei Huang T. Dahanayaka Kanchana Thilakarathna Philip H. W. Leong H. El Gamal 61 2 0 28 Mar 2023
GP-PCS: One-shot Feature-Preserving Point Cloud Simplification with Gaussian Processes on Riemannian Manifolds Stuti Pathak Thomas M. McDonald Seppe Sels R. Penne 3DPC 64 1 0 27 Mar 2023
Learning Generative Models with Goal-conditioned Reinforcement Learning Mariana Vargas Vieyra Pierre Ménard GAN 31 0 0 26 Mar 2023
Deep Kernel Methods Learn Better: From Cards to Process Optimization Mani Valleti Rama K Vasudevan M. Ziatdinov Sergei V. Kalinin BDL 36 9 0 25 Mar 2023
Variational Inference for Longitudinal Data Using Normalizing Flows Clément Chadebec S. Allassonnière BDL DRL 58 1 0 24 Mar 2023
Particle Mean Field Variational Bayes Minh-Ngoc Tran Paco Tseng Robert Kohn 77 3 0 24 Mar 2023
Reinforcement Learning with Exogenous States and Rewards George Trimponias Thomas G. Dietterich OffRL 65 2 0 22 Mar 2023
Training Invertible Neural Networks as Autoencoders The-Gia Leo Nguyen Lynton Ardizzone Ullrich Kothe BDL DRL SSL 69 9 0 20 Mar 2023
Convergence Analysis of Stochastic Gradient Descent with MCMC Estimators Tian-cheng Li Fan Chen Huajie Chen Zaiwen Wen 57 4 0 19 Mar 2023
Practical and Matching Gradient Variance Bounds for Black-Box Variational Bayesian Inference Kyurae Kim Kaiwen Wu Jisu Oh Jacob R. Gardner BDL 113 8 0 18 Mar 2023
An Approximate Bayesian Approach to Covariate-dependent Graphical Modeling Sutanoy Dasgupta P. Zhao Jacob Helwig P. Ghosh D. Pati Bani Mallick 55 1 0 15 Mar 2023
Efficient Bayesian Physics Informed Neural Networks for Inverse Problems via Ensemble Kalman Inversion Andrew Pensoneault Xueyu Zhu PINN 74 5 0 13 Mar 2023
Kernel Density Bayesian Inverse Reinforcement Learning Aishwarya Mandyam Didong Li Jiayu Yao Diana Cai Andrew Jones Barbara E. Engelhardt OffRL BDL 90 3 0 13 Mar 2023
Informative co-data learning for high-dimensional Horseshoe regression Claudio Busatto M. V. D. Wiel 122 2 0 10 Mar 2023
Federated Learning via Variational Bayesian Inference: Personalization, Sparsity and Clustering Xu Zhang Wenpeng Li Yunfeng Shao Yinchuan Li FedML 89 5 0 08 Mar 2023
DR-VIDAL -- Doubly Robust Variational Information-theoretic Deep Adversarial Learning for Counterfactual Prediction and Treatment Effect Estimation on Real World Data Shantanu Ghosh Zheng Feng Jiang Bian Kevin R. B. Butler M. Prosperi CML OOD BDL 23 0 0 07 Mar 2023
Variational Inference for Neyman-Scott Processes Chengkuan Hong C. Shelton BDL 57 2 0 07 Mar 2023
MFAI: A Scalable Bayesian Matrix Factorization Approach to Leveraging Auxiliary Information Zhiwei Wang Fa Zhang Conghui Zheng Xianghong Hu Mingxuan Cai Can Yang 43 1 0 05 Mar 2023
Lightweight, Uncertainty-Aware Conformalized Visual Odometry Alex C. Stutts Danilo Erricolo Theja Tulabandhula A. R. Trivedi UQCV 113 11 0 03 Mar 2023
Spectral learning of Bernoulli linear dynamical systems models Iris R. Stone Yotam Sagiv Il Memming Park Jonathan W. Pillow 92 1 0 03 Mar 2023
Learning Energy Conserving Dynamics Efficiently with Hamiltonian Gaussian Processes M. Ross Markus Heinonen 54 2 0 03 Mar 2023
BayeSeg: Bayesian Modeling for Medical Image Segmentation with Interpretable Generalizability Yuxin Li Hang Zhou Yibo Gao Xiahai Zhuang OOD 126 9 0 03 Mar 2023
Dimension-reduced KRnet maps for high-dimensional Bayesian inverse problems Yani Feng Keju Tang Xiaoliang Wan Qifeng Liao 58 2 0 01 Mar 2023
PixCUE: Joint Uncertainty Estimation and Image Reconstruction in MRI using Deep Pixel Classification Mevan Ekanayake Kamlesh Pawar Gary Egan Zhaolin Chen UQCV 57 0 0 28 Feb 2023
Efficient Sensor Placement from Regression with Sparse Gaussian Processes in Continuous and Discrete Spaces Kalvik Jakkala Srinivas Akella 77 1 0 28 Feb 2023
Modern Bayesian Experimental Design Tom Rainforth Adam Foster Desi R. Ivanova Freddie Bickford-Smith 125 88 0 28 Feb 2023
An Approximation Theory Framework for Measure-Transport Sampling Algorithms Ricardo Baptista Bamdad Hosseini Nikola B. Kovachki Youssef M. Marzouk A. Sagiv OT 98 17 0 27 Feb 2023
U-Statistics for Importance-Weighted Variational Inference Javier Burroni Kenta Takatsu Justin Domke Daniel Sheldon 63 1 0 27 Feb 2023
Natural Gradient Hybrid Variational Inference with Application to Deep Mixed Models Weiben Zhang M. Smith Worapree Maneesoonthorn Rubén Loaiza-Maya 33 1 0 27 Feb 2023
A Survey on Uncertainty Quantification Methods for Deep Learning Wenchong He Zhe Jiang Tingsong Xiao Zelin Xu Yukun Li BDL UQCV AI4CE 193 24 0 26 Feb 2023
Modulated Neural ODEs I. Auzina Çağatay Yıldız Sara Magliacane Matthias Bethge E. Gavves 81 6 0 26 Feb 2023
A Targeted Accuracy Diagnostic for Variational Approximations Yu Wang Mikolaj Kasprzak Jonathan H. Huggins DRL 69 2 0 24 Feb 2023
Streaming data recovery via Bayesian tensor train decomposition Yunyu Huang Yani Feng Qifeng Liao 8 0 0 23 Feb 2023
Distributional Learning of Variational AutoEncoder: Application to Synthetic Data Generation SeungHwan An Jong-June Jeon DRL 155 8 0 22 Feb 2023
VI-DGP: A variational inference method with deep generative prior for solving high-dimensional inverse problems Yingzhi Xia Qifeng Liao Jinglai Li 93 4 0 22 Feb 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 82 19 0 21 Feb 2023
The Shrinkage-Delinkage Trade-off: An Analysis of Factorized Gaussian Approximations for Variational Inference C. Margossian Lawrence K. Saul 83 8 0 17 Feb 2023
Piecewise Deterministic Markov Processes for Bayesian Neural Networks Ethan Goan Dimitri Perrin Kerrie Mengersen Clinton Fookes 71 0 0 17 Feb 2023
Conditional deep generative models as surrogates for spatial field solution reconstruction with quantified uncertainty in Structural Health Monitoring applications Nicholas E. Silionis Theodora Liangou K. Anyfantis AI4CE 133 1 0 14 Feb 2023