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Variational Inference: A Review for Statisticians

4 January 2016

Papers citing "Variational Inference: A Review for Statisticians"

50 / 1,838 papers shown

Title
A spectral regularisation framework for latent variable models designed for single channel applications Ryan Balshaw P. Heyns Daniel N. Wilke Stephan Schmidt 48 1 0 30 Oct 2023
Estimating the Rate-Distortion Function by Wasserstein Gradient Descent Yibo Yang Stephan Eckstein Marcel Nutz Stephan Mandt 79 10 0 29 Oct 2023
Variance-based sensitivity of Bayesian inverse problems to the prior distribution John E. Darges A. Alexanderian P. Gremaud 35 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 69 10 0 26 Oct 2023
Adaptive importance sampling for heavy-tailed distributions via $α$ -divergence minimization Thomas Guilmeau Nicola Branchini Émilie Chouzenoux Victor Elvira 83 2 0 25 Oct 2023
Particle-based Variational Inference with Generalized Wasserstein Gradient Flow Ziheng Cheng Shiyue Zhang Longlin Yu Cheng Zhang BDL 67 10 0 25 Oct 2023
Joint Distributional Learning via Cramer-Wold Distance SeungHwan An Jong-June Jeon 67 0 0 25 Oct 2023
Wasserstein Gradient Flow over Variational Parameter Space for Variational Inference Dai Hai Nguyen Tetsuya Sakurai Hiroshi Mamitsuka 139 2 0 25 Oct 2023
SEGO: Sequential Subgoal Optimization for Mathematical Problem-Solving Xueliang Zhao Xinting Huang Wei Bi Lingpeng Kong LRM 91 1 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 50 1 0 18 Oct 2023
On permutation symmetries in Bayesian neural network posteriors: a variational perspective Simone Rossi Ankit Singh T. Hannagan 71 3 0 16 Oct 2023
Sub-optimality of the Naive Mean Field approximation for proportional high-dimensional Linear Regression Jiaze Qiu 60 3 0 15 Oct 2023
An Introduction to the Calibration of Computer Models Richard D. Wilkinson Christopher W. Lanyon 63 0 0 13 Oct 2023
The surrogate Gibbs-posterior of a corrected stochastic MALA: Towards uncertainty quantification for neural networks S. Bieringer Gregor Kasieczka Maximilian F. Steffen Mathias Trabs 94 1 0 13 Oct 2023
Hamiltonian Dynamics of Bayesian Inference Formalised by Arc Hamiltonian Systems Takuo Matsubara 44 0 0 11 Oct 2023
Surrogate modeling for stochastic crack growth processes in structural health monitoring applications Nicholas E. Silionis K. Anyfantis AI4CE 74 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 84 12 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 90 7 0 06 Oct 2023
Accelerating optimization over the space of probability measures Shi Chen Wenxuan Wu Yuhang Yao Stephen J. Wright 99 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 77 15 0 05 Oct 2023
Cutting Feedback in Misspecified Copula Models Michael Stanley Smith Weichang Yu David J. Nott David T. Frazier 84 1 0 05 Oct 2023
Diffusion Generative Flow Samplers: Improving learning signals through partial trajectory optimization Dinghuai Zhang Ricky Tian Qi Chen Cheng-Hao Liu Aaron C. Courville Yoshua Bengio 137 49 0 04 Oct 2023
If there is no underfitting, there is no Cold Posterior Effect Yijie Zhang Yi-Shan Wu Luis A. Ortega A. Masegosa UQCV 70 1 0 02 Oct 2023
Drug Discovery with Dynamic Goal-aware Fragments Seul Lee Seanie Lee Kenji Kawaguchi Sung Ju Hwang 125 9 0 02 Oct 2023
Learning How to Propagate Messages in Graph Neural Networks Teng Xiao Zhengyu Chen Donglin Wang Suhang Wang GNN 101 80 0 01 Oct 2023
A General Offline Reinforcement Learning Framework for Interactive Recommendation Teng Xiao Donglin Wang OffRL 115 74 0 01 Oct 2023
Pointwise uncertainty quantification for sparse variational Gaussian process regression with a Brownian motion prior Luke Travis Kolyan Ray 95 4 0 29 Sep 2023
Stochastic Implicit Neural Signed Distance Functions for Safe Motion Planning under Sensing Uncertainty Carlos Quintero-Peña Wil Thomason Bo Xiong Anastasios Kyrillidis Lydia E. Kavraki 67 7 0 28 Sep 2023
A Variational Spike-and-Slab Approach for Group Variable Selection M. Ramezani Hossein Rastgoftar Jun S. Liu 65 0 0 28 Sep 2023
A Mean Field Approach to Empirical Bayes Estimation in High-dimensional Linear Regression Soumendu Sundar Mukherjee Bodhisattva Sen Subhabrata Sen 82 5 0 28 Sep 2023
Generating Personalized Insulin Treatments Strategies with Deep Conditional Generative Time Series Models Manuel Schürch Xiang Li Ahmed Allam Giulia Rathmes Amina Mollaysa Claudia Cavelti-Weder Michael Krauthammer AI4TS 70 4 0 28 Sep 2023
FG-NeRF: Flow-GAN based Probabilistic Neural Radiance Field for Independence-Assumption-Free Uncertainty Estimation Songlin Wei JIazhao Zhang Yang Wang Ruben Verborgh Hao Su He Wang AI4CE 72 3 0 28 Sep 2023
A Primer on Bayesian Neural Networks: Review and Debates Federico Danieli Konstantinos Pitas M. Vladimirova Vincent Fortuin BDL AAML 114 20 0 28 Sep 2023
Bayesian Personalized Federated Learning with Shared and Personalized Uncertainty Representations Hui Chen Hengyu Liu LongBing Cao Tiancheng Zhang FedML 98 3 0 27 Sep 2023
Improvements on Scalable Stochastic Bayesian Inference Methods for Multivariate Hawkes Process Alex Ziyu Jiang Abel Rodríguez 58 1 0 26 Sep 2023
Reparameterized Variational Rejection Sampling M. Jankowiak Du Phan DRL BDL 54 1 0 26 Sep 2023
Generative Filtering for Recursive Bayesian Inference with Streaming Data Ian Taylor Andee Kaplan Brenda Betancourt 57 0 0 25 Sep 2023
Independent projections of diffusions: Gradient flows for variational inference and optimal mean field approximations D. Lacker DiffM 70 9 0 23 Sep 2023
Bayesian sparsification for deep neural networks with Bayesian model reduction Dimitrije Marković K. Friston S. Kiebel BDL UQCV 76 2 0 21 Sep 2023
Deep Networks as Denoising Algorithms: Sample-Efficient Learning of Diffusion Models in High-Dimensional Graphical Models Song Mei Yuchen Wu DiffM 86 28 0 20 Sep 2023
Generalizing Across Domains in Diabetic Retinopathy via Variational Autoencoders Sharon Chokuwa M. H. Khan 95 5 0 20 Sep 2023
Conformalized Multimodal Uncertainty Regression and Reasoning Mimmo Parente Nastaran Darabi Alex C. Stutts Theja Tulabandhula A. R. Trivedi UQCV 84 8 0 20 Sep 2023
Group Spike and Slab Variational Bayes M. Komodromos Marina Evangelou Sarah Filippi Kolyan Ray 100 2 0 19 Sep 2023
Data-driven Modeling and Inference for Bayesian Gaussian Process ODEs via Double Normalizing Flows Jian Xu Shian Du Junmei Yang Xinghao Ding John Paisley Delu Zeng 75 0 0 17 Sep 2023
Total Variation Distance Meets Probabilistic Inference Arnab Bhattacharyya Sutanu Gayen Kuldeep S. Meel Dimitrios Myrisiotis A. Pavan N. V. Vinodchandran 37 4 0 17 Sep 2023
Beta Diffusion Mingyuan Zhou Tianqi Chen Zhendong Wang Huangjie Zheng DiffM 95 13 0 14 Sep 2023
All you need is spin: SU(2) equivariant variational quantum circuits based on spin networks R. D. East Guillermo Alonso-Linaje Chae-Yeun Park 52 13 0 13 Sep 2023
Dynamic Causal Disentanglement Model for Dialogue Emotion Detection Yuting Su Yichen Wei Weizhi Nie Sicheng Zhao Anan Liu 71 4 0 13 Sep 2023
Towards the TopMost: A Topic Modeling System Toolkit Xiaobao Wu Fengjun Pan Anh Tuan Luu 119 17 0 13 Sep 2023
Generalized Variable Selection Algorithms for Gaussian Process Models by LASSO-like Penalty Zhiyong Hu D. Dey 74 3 0 08 Sep 2023