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

359 papers, page 5 of 8

201. ICLR 2024Training Bayesian Neural Networks with Sparse Subspace Variational Inference
202. ICLR 2024Transport meets Variational Inference: Controlled Monte Carlo Diffusions
203. ICLR 2024Uncertainty Quantification via Stable Distribution Propagation
204. ICLR 2024Variational Bayesian Last Layers
205. ICLR 2024Variational Inference for SDEs Driven by Fractional Noise
206. ICML 2024$\mathtt{VITS}$ : Variational Inference Thompson Sampling for contextual bandits
207. ICML 2024Adaptive Observation Cost Control for Variational Quantum Eigensolvers
208. ICML 2024Amortized Variational Deep Kernel Learning
209. ICML 2024Batch and match: black-box variational inference with a score-based divergence
210. ICML 2024Beyond ELBOs: A Large-Scale Evaluation of Variational Methods for Sampling
211. ICML 2024Efficient Mixture Learning in Black-Box Variational Inference
212. ICML 2024Generalized Smooth Variational Inequalities: Methods with Adaptive Stepsizes
213. ICML 2024High-Probability Convergence for Composite and Distributed Stochastic Minimization and Variational Inequalities with Heavy-Tailed Noise
214. ICML 2024Kernel Semi-Implicit Variational Inference
215. ICML 2024Language-guided Skill Learning with Temporal Variational Inference
216. ICML 2024Latent Optimal Paths by Gumbel Propagation for Variational Bayesian Dynamic Programming
217. ICML 2024Learning Latent Structures in Network Games via Data-Dependent Gated-Prior Graph Variational Autoencoders
218. ICML 2024Logistic Variational Bayes Revisited
219. ICML 2024Online Variational Sequential Monte Carlo
220. ICML 2024Provably Scalable Black-Box Variational Inference with Structured Variational Families
221. ICML 2024Purify Unlearnable Examples via Rate-Constrained Variational Autoencoders
222. ICML 2024Reparameterized Importance Sampling for Robust Variational Bayesian Neural Networks
223. ICML 2024Self-Attention through Kernel-Eigen Pair Sparse Variational Gaussian Processes
224. ICML 2024Theoretical Guarantees for Variational Inference with Fixed-Variance Mixture of Gaussians
225. ICML 2024Understanding Stochastic Natural Gradient Variational Inference
226. ICML 2024Variational Inference with Coverage Guarantees in Simulation-Based Inference
227. ICML 2024Variational Learning is Effective for Large Deep Networks
228. ICML 2024Variational Linearized Laplace Approximation for Bayesian Deep Learning
229. ICML 2024Variational Partial Group Convolutions for Input-Aware Partial Equivariance of Rotations and Color-Shifts
230. ICML 2024Variational Schrödinger Diffusion Models
231. ICML 2024Why do Variational Autoencoders Really Promote Disentanglement?
232. NeurIPS 2024Asymptotics of Alpha-Divergence Variational Inference Algorithms with Exponential Families
233. NeurIPS 2024Doob's Lagrangian: A Sample-Efficient Variational Approach to Transition Path Sampling
234. NeurIPS 2024EigenVI: score-based variational inference with orthogonal function expansions
235. NeurIPS 2024Exploring Jacobian Inexactness in Second-Order Methods for Variational Inequalities: Lower Bounds, Optimal Algorithms and Quasi-Newton Approximations
236. NeurIPS 2024Fearless Stochasticity in Expectation Propagation
237. NeurIPS 2024Globally Convergent Variational Inference
238. NeurIPS 2024Inference of Neural Dynamics Using Switching Recurrent Neural Networks
239. NeurIPS 2024Last-Iterate Convergence for Generalized Frank-Wolfe in Monotone Variational Inequalities
240. NeurIPS 2024LiteVAE: Lightweight and Efficient Variational Autoencoders for Latent Diffusion Models
241. NeurIPS 2024Noether's Razor: Learning Conserved Quantities
242. NeurIPS 2024On Divergence Measures for Training GFlowNets
243. NeurIPS 2024Particle Semi-Implicit Variational Inference
244. NeurIPS 2024Poisson Variational Autoencoder
245. NeurIPS 2024Private Algorithms for Stochastic Saddle Points and Variational Inequalities: Beyond Euclidean Geometry
246. NeurIPS 2024Quadratic Quantum Variational Monte Carlo
247. NeurIPS 2024Rejection via Learning Density Ratios
248. NeurIPS 2024Training Binary Neural Networks via Gaussian Variational Inference and Low-Rank Semidefinite Programming
249. NeurIPS 2024TreeVI: Reparameterizable Tree-structured Variational Inference for Instance-level Correlation Capturing
250. NeurIPS 2024Unscrambling disease progression at scale: fast inference of event permutations with optimal transport

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.