Variational Inference papers, 2023–2026

Key findings

• Variational Inference fell from 106 papers in 2023 to 91 in 2026, so interest is cooling.
• NeurIPS publishes the most Variational Inference work: 124 papers from 2023 to 2026, 35% of the four-venue total.
• The fastest-growing overlap is with Sampling Methods: papers tagged with both rose from 1 in 2023 to 6 in 2026.
• Among the 5 topics under Probabilistic and Bayesian, it ranks #5 by 2026 paper count; the fastest grower in the group, Gaussian Processes, is up 2.2x versus 0.9x here.

Representative papers

Spread across years and venues so every edition shows up

• NeurIPS 2026A Reproducible Evaluation Protocol for Quantum Non-Linearity in Variational Quantum Models
• ICML 2026Beyond Independence: Learning Correlated Views for Variational Incomplete Multi-View Clustering
• ICLR 2026Optimizing Data Augmentation through Bayesian Model Selection
• CVPR 2026BrepVGAE Variational Graph Autoencoder with Unified Latent Representation for B-rep
• NeurIPS 2025Covariate-moderated Empirical Bayes Matrix Factorization
• ICML 2025A Variational Information Theoretic Approach to Out-of-Distribution Detection
• ICLR 2025Bridging the Gap between Variational Inference and Stochastic Gradient MCMC in Function Space
• CVPR 2025Dora: Sampling and Benchmarking for 3D Shape Variational Auto-Encoders
• NeurIPS 2024Training Binary Neural Networks via Gaussian Variational Inference and Low-Rank Semidefinite Programming
• ICML 2024Amortized Variational Deep Kernel Learning
• ICLR 2024Causal Inference with Conditional Front-Door Adjustment and Identifiable Variational Autoencoder
• CVPR 2024WorDepth: Variational Language Prior for Monocular Depth Estimation
• NeurIPS 2023[Re] Variational Neural Cellular Automata
• ICML 2023Coupled Variational Autoencoder
• ICLR 2023A VAE for Transformers with Nonparametric Variational Information Bottleneck
• CVPR 2023Confidence-Aware Personalized Federated Learning via Variational Expectation Maximization
• NeurIPS 2026Adaptive Delayed-Update Cyclic Algorithm for Variational Inequalities
• ICML 2026Dual-stage Contrastive Learning-enhanced Multi-view Variational Clustering
• ICLR 2026Pareto Variational Autoencoder
• CVPR 2026Variational Graph-based Normal Integration

All Variational Inference papers

44 papers, page 1 of 1

1. NeurIPS 2023[Re] Variational Neural Cellular Automata
2. NeurIPS 2023A Finite-Particle Convergence Rate for Stein Variational Gradient Descent
3. NeurIPS 2023A Heavy-Tailed Algebra for Probabilistic Programming
4. NeurIPS 2023A Variational Perspective on High-Resolution ODEs
5. NeurIPS 2023AdaVAE: Bayesian Structural Adaptation for Variational Autoencoders
6. NeurIPS 2023Alpha-divergence Variational Inference Meets Importance Weighted Auto-Encoders: Methodology and Asymptotics
7. NeurIPS 2023Amortized Reparametrization: Efficient and Scalable Variational Inference for Latent SDEs
8. NeurIPS 2023Approximate inference of marginals using the IBIA framework
9. NeurIPS 2023ARTree: A Deep Autoregressive Model for Phylogenetic Inference
10. NeurIPS 2023Attention as Implicit Structural Inference
11. NeurIPS 2023Conditional Matrix Flows for Gaussian Graphical Models
12. NeurIPS 2023Conformalized matrix completion
13. NeurIPS 2023Discriminative Calibration: Check Bayesian Computation from Simulations and Flexible Classifier
14. NeurIPS 2023Embracing the chaos: analysis and diagnosis of numerical instability in variational flows
15. NeurIPS 2023End-To-End Latent Variational Diffusion Models for Inverse Problems in High Energy Physics
16. NeurIPS 2023Estimating Noise Correlations Across Continuous Conditions With Wishart Processes
17. NeurIPS 2023Function Space Bayesian Pseudocoreset for Bayesian Neural Networks
18. NeurIPS 2023Gacs-Korner Common Information Variational Autoencoder
19. NeurIPS 2023GeoPhy: Differentiable Phylogenetic Inference via Geometric Gradients of Tree Topologies
20. NeurIPS 2023Hierarchical Semi-Implicit Variational Inference with Application to Diffusion Model Acceleration
21. NeurIPS 2023Joint Prompt Optimization of Stacked LLMs using Variational Inference
22. NeurIPS 2023Latent SDEs on Homogeneous Spaces
23. NeurIPS 2023On permutation symmetries in Bayesian neural network posteriors: a variational perspective
24. NeurIPS 2023On the Convergence of Black-Box Variational Inference
25. NeurIPS 2023On the Statistical Consistency of Risk-Sensitive Bayesian Decision-Making
26. NeurIPS 2023Optimal Extragradient-Based Algorithms for Stochastic Variational Inequalities with Separable Structure
27. NeurIPS 2023Particle-based Variational Inference with Generalized Wasserstein Gradient Flow
28. NeurIPS 2023Penguin: Parallel-Packed Homomorphic Encryption for Fast Graph Convolutional Network Inference
29. NeurIPS 2023Pointwise uncertainty quantification for sparse variational Gaussian process regression with a Brownian motion prior
30. NeurIPS 2023Provable convergence guarantees for black-box variational inference
31. NeurIPS 2023Provably Fast Finite Particle Variants of SVGD via Virtual Particle Stochastic Approximation
32. NeurIPS 2023Pseudo-Likelihood Inference
33. NeurIPS 2023Score-based Data Assimilation
34. NeurIPS 2023Single-Call Stochastic Extragradient Methods for Structured Non-monotone Variational Inequalities: Improved Analysis under Weaker Conditions
35. NeurIPS 2023Statistical Guarantees for Variational Autoencoders using PAC-Bayesian Theory
36. NeurIPS 2023Sub-optimality of the Naive Mean Field approximation for proportional high-dimensional Linear Regression
37. NeurIPS 2023Towards Understanding the Dynamics of Gaussian-Stein Variational Gradient Descent
38. NeurIPS 2023Variational Gaussian processes for linear inverse problems
39. NeurIPS 2023Variational Gaussian Processes with Decoupled Conditionals
40. NeurIPS 2023Variational Gibbs Inference for Statistical Model Estimation from Incomplete Data
41. NeurIPS 2023Variational Imbalanced Regression: Fair Uncertainty Quantification via Probabilistic Smoothing
42. NeurIPS 2023Variational Inference with Gaussian Score Matching
43. NeurIPS 2023Variational Weighting for Kernel Density Ratios
44. NeurIPS 2023VaRT: Variational Regression Trees

FAQ

How many Variational Inference papers were published at NeurIPS, ICLR, ICML and CVPR from 2023 to 2026?

359 in total: 106 in 2023, 76 in 2024, 86 in 2025, 91 in 2026.

How many Variational Inference papers are at NeurIPS 2026?

NeurIPS 2026 has 30 Variational Inference papers, versus 28 the year before.

How many Variational Inference papers are at ICLR 2026?

ICLR 2026 has 23 Variational Inference papers, versus 28 the year before.

How many Variational Inference papers are at ICML 2026?

ICML 2026 has 35 Variational Inference papers, versus 28 the year before.

How many Variational Inference papers are at CVPR 2026?

CVPR 2026 has 3 Variational Inference papers, versus 2 the year before.

Which conference publishes the most Variational Inference papers?

NeurIPS, with 124 papers from 2023 to 2026.