conftrace_
← Bayesian & Probabilistic
Artificial Intelligence › Bayesian & Probabilistic ›

Bayesian Learning

1,663 papers

Papers per year

2
48
52
137
36
'05
'10
'15
'20
'25

Papers

Scalable Bayes via Barycenter in Wasserstein Space JMLR 2018
ELFI: Engine for Likelihood-Free Inference JMLR 2018
Markov Blanket and Markov Boundary of Multiple Variables JMLR 2018
Covariances, Robustness, and Variational Bayes JMLR 2018
Bayesian Trees for Automated Cytometry Data Analysis MLHC 2018
Pattern-Based Behavioural Analysis on Neurosurgical Simulation Data MLHC 2018
Bayesian Network Classifiers Under the Ensemble Perspective PGM 2018
A Bayesian Approach for Inferring Local Causal Structure in Gene Regulatory Networks PGM 2018
Instance-Specific Bayesian Network Structure Learning PGM 2018
Learning Bayesian network classifiers with completed partially directed acyclic graphs PGM 2018
Learning Bayesian Networks by Branching on Constraints PGM 2018
Adaptive Bayesian Sampling with Monte Carlo EM NIPS 2017
Convolutional Gaussian Processes NIPS 2017
Model evidence from nonequilibrium simulations NIPS 2017
Fast amortized inference of neural activity from calcium imaging data with variational autoencoders NIPS 2017
Bayesian Optimization with Gradients NIPS 2017
Bayesian Inference of Individualized Treatment Effects using Multi-task Gaussian Processes NIPS 2017
Linearly constrained Gaussian processes NIPS 2017
Learning from uncertain curves: The 2-Wasserstein metric for Gaussian processes NIPS 2017
Concrete Dropout NIPS 2017
Deep Multi-task Gaussian Processes for Survival Analysis with Competing Risks NIPS 2017
Gauging Variational Inference NIPS 2017
Scalable Log Determinants for Gaussian Process Kernel Learning NIPS 2017
Scalable Model Selection for Belief Networks NIPS 2017
Flexible statistical inference for mechanistic models of neural dynamics NIPS 2017