Key papers

• ImprovedVBGS: Real-time Continual Variational Bayes Gaussian Splatting (2026)
  Damani Mguni-Coker
  5.01
• Missing Data Imputation under Manifold Hypothesis (2026)
  Zelong Bi et al.
  2.00
• A Generative Framework for Causal Estimation via Importance-Weighted Diffusion Distillation (2025)
  Xinran Song and Tianyu Chen and Mingyuan Zhou
  1.22
• Compositional Model based Fisher Vector Coding for Image Classification (2016)
  Lingqiao Liu et al.
  —
• Overdispersed Black-Box Variational Inference (2016)
  Francisco J. R. Ruiz et al.
  —
• Online but Accurate Inference for Latent Variable Models with Local Gibbs Sampling (2016)
  Christophe Dupuy (SIERRA) et al.
  —
• Structured and Efficient Variational Deep Learning with Matrix Gaussian Posteriors (2016)
  Christos Louizos and Max Welling
  —
• Gaussian variational approximation with sparse precision matrices (2016)
  Linda S. L. Tan and David J. Nott
  —
• Variational hybridization and transformation for large inaccurate noisy-or networks (2016)
  Yusheng Xie et al.
  —
• Deep Variational Bayes Filters: Unsupervised Learning of State Space Models from Raw Data (2016)
  Maximilian Karl et al.
  —
• Relevant sparse codes with variational information bottleneck (2016)
  Matthew Chalk et al.
  —
• Variational Bayesian Inference for Hidden Markov Models With Multivariate Gaussian Output Distributions (2016)
  Christian Gruhl et al.
  —
• Understanding Probabilistic Sparse Gaussian Process Approximations (2016)
  Matthias Bauer et al.
  —
• Fast robustness quantification with variational Bayes (2016)
  Ryan Giordano et al.
  —
• Automatic Variational ABC (2016)
  Alexander Moreno et al.
  —
• Scaling Factorial Hidden Markov Models: Stochastic Variational Inference without Messages (2016)
  Yin Cheng Ng et al.
  —
• Variational Gaussian Process Auto-Encoder for Ordinal Prediction of Facial Action Units (2016)
  Stefanos Eleftheriadis et al.
  —
• Variational Inference with Hamiltonian Monte Carlo (2016)
  Christopher Wolf et al.
  —
• The Generalized Reparameterization Gradient (2016)
  Francisco J. R. Ruiz and Michalis K. Titsias and David M. Blei
  —
• Truncated Variational Expectation Maximization (2016)
  J\"org L\"ucke
  —
• Fast Bayesian Non-Negative Matrix Factorisation and Tri-Factorisation (2016)
  Thomas Brouwer et al.
  —
• Variational Inference via $\chi$-Upper Bound Minimization (2016)
  Adji B. Dieng et al.
  —
• Stochastic Variational Deep Kernel Learning (2016)
  Andrew Gordon Wilson et al.
  —
• Categorical Reparameterization with Gumbel-Softmax (2016)
  Eric Jang et al.
  —
• Boosting Variational Inference (2016)
  Fangjian Guo et al.
  —
• Variational Boosting: Iteratively Refining Posterior Approximations (2016)
  Andrew C. Miller et al.
  —
• Variational Fourier features for Gaussian processes (2016)
  James Hensman et al.
  —
• Two Methods For Wild Variational Inference (2016)
  Qiang Liu et al.
  —
• Truncation-free Hybrid Inference for DPMM (2017)
  Arnim Bleier
  —
• Gaussian-Dirichlet Posterior Dominance in Sequential Learning (2017)
  Ian Osband and Benjamin Van Roy
  —
• A Mutually-Dependent Hadamard Kernel for Modelling Latent Variable Couplings (2017)
  Sami Remes et al.
  —
• The variational Laplace approach to approximate Bayesian inference (2017)
  Jean Daunizeau
  —
• Efficient variational Bayesian neural network ensembles for outlier detection (2017)
  Nick Pawlowski et al.
  —
• Learning Approximately Objective Priors (2017)
  Eric Nalisnick et al.
  —
• Parametric Gaussian Process Regression for Big Data (2017)
  Maziar Raissi
  —
• $k$-means as a variational EM approximation of Gaussian mixture models (2017)
  J\"org L\"ucke et al.
  —
• Importance Sampled Stochastic Optimization for Variational Inference (2017)
  Joseph Sakaya and Arto Klami
  —
• Proximity Variational Inference (2017)
  Jaan Altosaar et al.
  —
• Doubly Stochastic Variational Inference for Deep Gaussian Processes (2017)
  Hugh Salimbeni et al.
  —
• Kernel Implicit Variational Inference (2017)
  Jiaxin Shi et al.
  —
• Variational Sequential Monte Carlo (2017)
  Christian A. Naesseth and Scott W. Linderman and Rajesh Ranganath and David M. Blei
  —
• Stochastic Gradient Monomial Gamma Sampler (2017)
  Yizhe Zhang et al.
  —
• Generative Models for Learning from Crowds (2017)
  Chi Hong
  —
• A Divergence Bound for Hybrids of MCMC and Variational Inference and an Application to Langevin Dynamics and SGVI (2017)
  Justin Domke
  —
• A Contemporary Overview of Probabilistic Latent Variable Models (2017)
  Rick Farouni
  —
• Concentration of tempered posteriors and of their variational approximations (2017)
  Pierre Alquier and James Ridgway
  —
• Learning Model Reparametrizations: Implicit Variational Inference by Fitting MCMC distributions (2017)
  Michalis K. Titsias
  —
• A deep generative model for gene expression profiles from single-cell RNA sequencing (2017)
  Romain Lopez et al.
  —
• $\alpha$-Variational Inference with Statistical Guarantees (2017)
  Yun Yang and Debdeep Pati and Anirban Bhattacharya
  —
• Scalable Gaussian Processes with Billions of Inducing Inputs via Tensor Train Decomposition (2017)
  Pavel Izmailov et al.
  —
• Structured Variational Inference for Coupled Gaussian Processes (2017)
  Vincent Adam
  —
• Neural Variational Inference and Learning in Undirected Graphical Models (2017)
  Volodymyr Kuleshov et al.
  —
• Recency-weighted Markovian inference (2017)
  Kristjan Kalm
  —
• Advances in Variational Inference (2017)
  Cheng Zhang et al.
  —
• Variational Bayesian Inference For A Scale Mixture Of Normal Distributions Handling Missing Data (2017)
  G. Revillon et al.
  —
• Embedding Words as Distributions with a Bayesian Skip-gram Model (2017)
  Arthur Bra\v{z}inskas et al.
  —
• Variational Deep Q Network (2017)
  Yunhao Tang and Alp Kucukelbir
  —
• Noisy Natural Gradient as Variational Inference (2017)
  Guodong Zhang and Shengyang Sun and David Duvenaud and Roger Grosse
  —
• Variational Inference over Non-differentiable Cardiac Simulators using Bayesian Optimization (2017)
  Adam McCarthy et al.
  —
• Gauged Mini-Bucket Elimination for Approximate Inference (2018)
  Sungsoo Ahn et al.
  —