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Bayesian Inference

1724 directly classified papers

Papers per year

Papers

Bayesian Domain Adaptation with Gaussian Mixture Domain-Indexing NIPS 2024
Amortizing intractable inference in diffusion models for vision, language, and control NIPS 2024
A Metalearned Neural Circuit for Nonparametric Bayesian Inference NIPS 2024
Sample-efficient Bayesian Optimisation Using Known Invariances NIPS 2024
Diffusion Priors for Variational Likelihood Estimation and Image Denoising NIPS 2024
Adaptive Batch Sizes for Active Learning: A Probabilistic Numerics Approach AISTATS 2024
Constrained Sampling with Primal-Dual Langevin Monte Carlo NIPS 2024
Parallel-in-Time Probabilistic Numerical ODE Solvers JMLR 2024
Streaming Bayes GFlowNets NIPS 2024
Poisson Variational Autoencoder NIPS 2024
SEL-BALD: Deep Bayesian Active Learning with Selective Labels NIPS 2024
Probabilistic size-and-shape functional mixed models NIPS 2024
Hamiltonian Monte Carlo Inference of Marginalized Linear Mixed-Effects Models NIPS 2024
Conditional Velocity Score Estimation for Image Restoration WACV 2024
Risk-sensitive control as inference with Rényi divergence NIPS 2024
A Framework for Improving the Reliability of Black-box Variational Inference JMLR 2024
Tensor-train methods for sequential state and parameter learning in state-space models JMLR 2024
Flexible Bayesian Product Mixture Models for Vector Autoregressions JMLR 2024
Nonasymptotic analysis of Stochastic Gradient Hamiltonian Monte Carlo under local conditions for nonconvex optimization JMLR 2024
On the Computational Complexity of Metropolis-Adjusted Langevin Algorithms for Bayesian Posterior Sampling JMLR 2024
PAC-Bayes-Chernoff bounds for unbounded losses NIPS 2024
Globally Convergent Variational Inference NIPS 2024
MAP- and MLE-Based Teaching JMLR 2024
Nonparametric Copula Models for Multivariate, Mixed, and Missing Data JMLR 2024
On Unbiased Estimation for Partially Observed Diffusions JMLR 2024