Variational Inference: A Review for Statisticians

4 January 2016

Papers citing "Variational Inference: A Review for Statisticians"

50 / 1,818 papers shown

Title
On the Impact of Sampling on Deep Sequential State Estimation Helena Calatrava R. Borsoi Tales Imbiriba Pau Closas BDL 20 0 0 28 Nov 2023
Variational Inference for the Latent Shrinkage Position Model Xian Yao Gwee I. C. Gormley Michael Fop 11 0 0 28 Nov 2023
A statistical approach to latent dynamic modeling with differential equations Maren Hackenberg Astrid Pechmann Clemens Kreutz Janbernd Kirschner Harald Binder 20 1 0 27 Nov 2023
Robust Errant Beam Prognostics with Conditional Modeling for Particle Accelerators Kishansingh Rajput Malachi Schram Willem Blokland Yasir Alanazi Pradeep Ramuhalli Alexander Zhukov Charles Peters Ricardo Vilalta 35 5 0 22 Nov 2023
On the Out-of-Distribution Coverage of Combining Split Conformal Prediction and Bayesian Deep Learning Paul Scemama Ariel Kapusta 48 0 0 21 Nov 2023
Variational Elliptical Processes Maria B˙ankestad Jens Sjölund Jalil Taghia Thomas B. Schon 36 2 0 21 Nov 2023
hvEEGNet: exploiting hierarchical VAEs on EEG data for neuroscience applications Giulia Cisotto Alberto Zancanaro I. Zoppis Sara Manzoni 30 1 0 20 Nov 2023
SpACNN-LDVAE: Spatial Attention Convolutional Latent Dirichlet Variational Autoencoder for Hyperspectral Pixel Unmixing S. Chitnis Kiran Mantripragada Faisal Z. Qureshi 24 0 0 17 Nov 2023
Fuse It or Lose It: Deep Fusion for Multimodal Simulation-Based Inference Marvin Schmitt Stefan T. Radev Paul-Christian Bürkner 60 5 0 17 Nov 2023
vEEGNet: learning latent representations to reconstruct EEG raw data via variational autoencoders Alberto Zancanaro Giulia Cisotto I. Zoppis Sara Manzoni DRL 21 3 0 16 Nov 2023
On the Quantification of Image Reconstruction Uncertainty without Training Data Sirui Bi Victor Fung Jiaxin Zhang 26 1 0 16 Nov 2023
Modeling Complex Disease Trajectories using Deep Generative Models with Semi-Supervised Latent Processes Cécile Trottet Manuel Schürch Ahmed Allam Imon Barua L. Petelytska Oliver Distler A. Hoffmann-Vold Michael Krauthammer Eustar collaborators 43 2 0 14 Nov 2023
Data-Efficient Task Generalization via Probabilistic Model-based Meta Reinforcement Learning Arjun Bhardwaj Jonas Rothfuss Bhavya Sukhija Yarden As Marco Hutter Stelian Coros Andreas Krause 32 5 0 13 Nov 2023
Optimal simulation-based Bayesian decisions Justin Alsing Thomas D. P. Edwards Benjamin Dan Wandelt 36 1 0 09 Nov 2023
Conditional Optimal Transport on Function Spaces Bamdad Hosseini Alexander W. Hsu Amirhossein Taghvaei OT 45 14 0 09 Nov 2023
Inferring stochastic rates from heterogeneous snapshots of particle positions Christopher E Miles Scott A. McKinley Fangyuan Ding R. Lehoucq 19 4 0 08 Nov 2023
Generative learning for nonlinear dynamics William Gilpin AI4CE PINN 65 27 0 07 Nov 2023
Understanding Tool Discovery and Tool Innovation Using Active Inference Poppy Collis Paul F Kinghorn Christopher L. Buckley 17 0 0 07 Nov 2023
BOB: Bayesian Optimized Bootstrap for Uncertainty Quantification in Gaussian Mixture Models Santiago Marin Bronwyn Loong A. Westveld 26 0 0 07 Nov 2023
Riemannian Laplace Approximation with the Fisher Metric Hanlin Yu Marcelo Hartmann Bernardo Williams Mark Girolami Arto Klami 32 3 0 05 Nov 2023
Forward $χ^2$ Divergence Based Variational Importance Sampling Chengrui Li Yule Wang Weihan Li Anqi Wu BDL 30 2 0 04 Nov 2023
Uncertainty Quantification of Deep Learning for Spatiotemporal Data: Challenges and Opportunities Wenchong He Zhe Jiang 35 1 0 04 Nov 2023
Variational Inference for Sparse Poisson Regression Mitra Kharabati Morteza Amini 37 1 0 02 Nov 2023
ABC-based Forecasting in State Space Models Chaya Weerasinghe Rubén Loaiza-Maya G. Martin David T. Frazier 16 1 0 02 Nov 2023
Rethinking Variational Inference for Probabilistic Programs with Stochastic Support Tim Reichelt C. Ong Tom Rainforth 30 2 0 01 Nov 2023
Diffusion models for probabilistic programming Simon Dirmeier Fernando Pérez-Cruz 58 0 0 01 Nov 2023
Variational non-Bayesian inference of the Probability Density Function in the Wiener Algebra U. J. Choi Kyung Soo Rim 14 0 0 01 Nov 2023
Bridging the Gap Between Variational Inference and Wasserstein Gradient Flows Mingxuan Yi Song Liu DRL 33 8 0 31 Oct 2023
On Feynman--Kac training of partial Bayesian neural networks Zheng Zhao Sebastian Mair Thomas B. Schon Jens Sjölund 40 0 0 30 Oct 2023
A spectral regularisation framework for latent variable models designed for single channel applications Ryan Balshaw P. Heyns Daniel N. Wilke Stephan Schmidt 37 1 0 30 Oct 2023
Estimating the Rate-Distortion Function by Wasserstein Gradient Descent Yibo Yang Stephan Eckstein Marcel Nutz Stephan Mandt 27 9 0 29 Oct 2023
Variance-based sensitivity of Bayesian inverse problems to the prior distribution John E. Darges A. Alexanderian P. Gremaud 15 1 0 27 Oct 2023
Hierarchical Semi-Implicit Variational Inference with Application to Diffusion Model Acceleration Longlin Yu Tianyu Xie Yu Zhu Tong Yang Xiangyu Zhang Cheng Zhang DiffM 24 6 0 26 Oct 2023
Adaptive importance sampling for heavy-tailed distributions via $α$ -divergence minimization Thomas Guilmeau Nicola Branchini Émilie Chouzenoux Victor Elvira 44 2 0 25 Oct 2023
Particle-based Variational Inference with Generalized Wasserstein Gradient Flow Ziheng Cheng Shiyue Zhang Longlin Yu Cheng Zhang BDL 32 6 0 25 Oct 2023
Joint Distributional Learning via Cramer-Wold Distance SeungHwan An Jong-June Jeon 32 0 0 25 Oct 2023
Wasserstein Gradient Flow over Variational Parameter Space for Variational Inference Dai Hai Nguyen Tetsuya Sakurai Hiroshi Mamitsuka 45 0 0 25 Oct 2023
SEGO: Sequential Subgoal Optimization for Mathematical Problem-Solving Xueliang Zhao Xinting Huang Wei Bi Lingpeng Kong LRM 48 0 0 19 Oct 2023
Subject-specific Deep Neural Networks for Count Data with High-cardinality Categorical Features Hangbin Lee I. Ha Changha Hwang Youngjo Lee 28 1 0 18 Oct 2023
On permutation symmetries in Bayesian neural network posteriors: a variational perspective Simone Rossi Ankit Singh T. Hannagan 32 2 0 16 Oct 2023
Sub-optimality of the Naive Mean Field approximation for proportional high-dimensional Linear Regression Jiaze Qiu 33 3 0 15 Oct 2023
Statistical guarantees for stochastic Metropolis-Hastings S. Bieringer Gregor Kasieczka Maximilian F. Steffen Mathias Trabs 36 1 0 13 Oct 2023
An Introduction to the Calibration of Computer Models Richard D. Wilkinson Christopher W. Lanyon 32 0 0 13 Oct 2023
Hamiltonian Dynamics of Bayesian Inference Formalised by Arc Hamiltonian Systems Takuo Matsubara 13 0 0 11 Oct 2023
Surrogate modeling for stochastic crack growth processes in structural health monitoring applications Nicholas E. Silionis K. Anyfantis AI4CE 26 0 0 11 Oct 2023
Learning Stackable and Skippable LEGO Bricks for Efficient, Reconfigurable, and Variable-Resolution Diffusion Modeling Huangjie Zheng Zhendong Wang Jianbo Yuan Guanghan Ning Pengcheng He Quanzeng You Hongxia Yang Mingyuan Zhou 40 9 0 10 Oct 2023
Leveraging Self-Consistency for Data-Efficient Amortized Bayesian Inference Marvin Schmitt Desi R. Ivanova Daniel Habermann Baixu Chen Jie Jiang Stefan T. Radev FedML 40 5 0 06 Oct 2023
Accelerating optimization over the space of probability measures Shi Chen Wenxuan Wu Yuhang Yao Stephen J. Wright 32 5 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 30 13 0 05 Oct 2023
Cutting Feedback in Misspecified Copula Models Michael Stanley Smith Weichang Yu David J. Nott David T. Frazier 24 1 0 05 Oct 2023