| Compiling Stan to Generative Probabilistic Languages and Extension to Deep Probabilistic Programming | Sep 30, 2018 | Probabilistic ProgrammingRepresentation Learning | CodeCode Available | 0 | 5 |
| Approximate Variational Inference Based on a Finite Sample of Gaussian Latent Variables | Jun 11, 2019 | Bayesian InferenceVariational Inference | CodeCode Available | 0 | 5 |
| Bayesian Semi-supervised Learning with Graph Gaussian Processes | Sep 12, 2018 | Active LearningGaussian Processes | CodeCode Available | 0 | 5 |
| Adaptive Robust Learning using Latent Bernoulli Variables | Dec 1, 2023 | Variational Inference | CodeCode Available | 0 | 5 |
| Manifold Gaussian Variational Bayes on the Precision Matrix | Oct 26, 2022 | Variational Inference | CodeCode Available | 0 | 5 |
| Approximate Message Passing for Bayesian Neural Networks | Jan 26, 2025 | Uncertainty QuantificationVariational Inference | CodeCode Available | 0 | 5 |
| Estimating treatment effects from single-arm trials via latent-variable modeling | Nov 6, 2023 | Variational Inference | CodeCode Available | 0 | 5 |
| Exchangeable modelling of relational data: checking sparsity, train-test splitting, and sparse exchangeable Poisson matrix factorization | Dec 6, 2017 | Variational Inference | CodeCode Available | 0 | 5 |
| Factorized Fusion Shrinkage for Dynamic Relational Data | Sep 30, 2022 | Variational Inference | CodeCode Available | 0 | 5 |
| Ensemble Kalman Variational Objectives: Nonlinear Latent Trajectory Inference with A Hybrid of Variational Inference and Ensemble Kalman Filter | Oct 17, 2020 | DiversityState Space Models | CodeCode Available | 0 | 5 |
