conftrace_
← Bayesian & Probabilistic
Machine Learning › Bayesian & Probabilistic ›

Variational Inference

802 papers

Papers per year

3
14
33
100
18
'05
'10
'15
'20
'25

Papers

Benefits of Non-Linear Scale Parameterizations in Black Box Variational Inference through Smoothness Results and Gradient Variance Bounds AISTATS 2024
Adaptive importance sampling for heavy-tailed distributions via $α$-divergence minimization AISTATS 2024
Efficient Variational Sequential Information Control AISTATS 2024
Think Global, Adapt Local: Learning Locally Adaptive K-Nearest Neighbor Kernel Density Estimators AISTATS 2024
Sequential Monte Carlo for Inclusive KL Minimization in Amortized Variational Inference AISTATS 2024
Towards Generalizable and Interpretable Motion Prediction: A Deep Variational Bayes Approach AISTATS 2024
Sample-efficient neural likelihood-free Bayesian inference of implicit HMMs AISTATS 2024
Normalizing Flows on the Product Space of SO(3) Manifolds for Probabilistic Human Pose Modeling CVPR 2024
DiVERT: Distractor Generation with Variational Errors Represented as Text for Math Multiple-choice Questions EMNLP 2024
Putting Back the Stops: Integrating Syntax with Neural Topic Models IJCAI 2024
Building Expressive and Tractable Probabilistic Generative Models: A Review IJCAI 2024
Variational Estimators of the Degree-corrected Latent Block Model for Bipartite Networks JMLR 2024
A flexible empirical Bayes approach to multiple linear regression and connections with penalized regression JMLR 2024
A Framework for Improving the Reliability of Black-box Variational Inference JMLR 2024
Wasserstein Proximal Coordinate Gradient Algorithms JMLR 2024
Measuring Sample Quality in Algorithms for Intractable Normalizing Function Problems JMLR 2024
On the Quantification of Image Reconstruction Uncertainty Without Training Data WACV 2024
Moment Matching Denoising Gibbs Sampling NIPS 2023
Conditional score-based diffusion models for Bayesian inference in infinite dimensions NIPS 2023
Integration-free Training for Spatio-temporal Multimodal Covariate Deep Kernel Point Processes NIPS 2023
Conditional Matrix Flows for Gaussian Graphical Models NIPS 2023
Undirected Probabilistic Model for Tensor Decomposition NIPS 2023
A Finite-Particle Convergence Rate for Stein Variational Gradient Descent NIPS 2023
Variational Gaussian processes for linear inverse problems NIPS 2023
Variational Inference with Gaussian Score Matching NIPS 2023