# Variational Inference

Variational inference (VI) is a powerful family of approximate Bayesian inference methods aiming to efficiently estimate complex probability distributions, often encountered in machine learning and scientific modeling. Current research focuses on improving VI's scalability and accuracy through novel algorithms like stochastic variance reduction, amortized inference, and the use of advanced model architectures such as Gaussian processes, Bayesian neural networks, and mixture models, often within the context of specific applications like anomaly detection and inverse problems. The resulting advancements in VI are significantly impacting various fields, enabling more robust uncertainty quantification, improved model interpretability, and efficient solutions to previously intractable problems in areas ranging from 3D scene modeling to causal discovery.

## Papers

### Batch, match, and patch: low-rank approximations for score-based variational inference

Chirag Modi, Diana Cai, Lawrence K. Saul

### Variational inference for pile-up removal at hadron colliders with diffusion models

Malte Algren, Christopher Pollard, John Andrew Raine, Tobias Golling

### SimSiam Naming Game: A Unified Approach for Representation Learning and Emergent Communication

Nguyen Le Hoang, Tadahiro Taniguchi, Fang Tianwei, Akira Taniguchi