posterior approximation

Bayesian Adaptation Gym: A Benchmark for the Bayesian Low-Rank Adaptation of Multi-Modal Language Models UAI 2026 Implicit Variational Rejection Sampling UAI 2026 Online Bernstein-von Mises theorem JMLR 2026 Spatial-Temporal Feedback Diffusion Guidance for Controlled Traffic Imputation AAAI 2026 A Mixture-Based Framework for Guiding Diffusion Models ICML 2025 Bayesian Low-Rank Learning (Bella): A Practical Approach to Bayesian Neural Networks AAAI 2025 Conditional Wasserstein Distances with Applications in Bayesian OT Flow Matching JMLR 2025 Noise-Aware Differentially Private Variational Inference AISTATS 2025 Scalable and Adaptive Variational Bayes Methods for Hawkes Processes JMLR 2025 Scalable Bayesian Learning with posteriors ICLR 2025 SoftCVI: Contrastive variational inference with self-generated soft labels ICLR 2025 Syntactic and Semantic Control of Large Language Models via Sequential Monte Carlo ICLR 2025 Training-Free Medical Image Inverses via Bi-Level Guided Diffusion Models WACV 2025 Tuning-Free Coreset Markov Chain Monte Carlo via Hot DoG UAI 2025 Wasserstein Gradient Flow over Variational Parameter Space for Variational Inference AISTATS 2025 Benefits of Non-Linear Scale Parameterizations in Black Box Variational Inference through Smoothness Results and Gradient Variance Bounds AISTATS 2024 Black Box Variational Inference with a Deterministic Objective: Faster, More Accurate, and Even More Black Box JMLR 2024 Coreset Markov chain Monte Carlo AISTATS 2024 Generalized Variational Inference via Optimal Transport AAAI 2024 Implicit Neural Representation Inference for Low-Dimensional Bayesian Deep Learning ICLR 2024 Learning Sparse Codes with Entropy-Based ELBOs AISTATS 2024 Low-rank Variational Bayes correction to the Laplace method JMLR 2024 Nonparametric Automatic Differentiation Variational Inference with Spline Approximation AISTATS 2024 Reparameterized Variational Rejection Sampling AISTATS 2024 Riemannian Laplace Approximation with the Fisher Metric AISTATS 2024