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Bayesian Inference

1724 directly classified papers

Papers per year

Papers

A Bayesian Perspective on Training Speed and Model Selection NIPS 2020
Bayesian Pseudocoresets NIPS 2020
Deep Rao-Blackwellised Particle Filters for Time Series Forecasting NIPS 2020
Posterior Re-calibration for Imbalanced Datasets NIPS 2020
Neuronal Gaussian Process Regression NIPS 2020
Variational Bayesian Monte Carlo with Noisy Likelihoods NIPS 2020
Learning under Model Misspecification: Applications to Variational and Ensemble methods NIPS 2020
Thompson Sampling via Local Uncertainty ICML 2020
Towards Scalable Bayesian Learning of Causal DAGs NIPS 2020
Probabilistic Linear Solvers for Machine Learning NIPS 2020
Analysis and Design of Thompson Sampling for Stochastic Partial Monitoring NIPS 2020
On the Expressiveness of Approximate Inference in Bayesian Neural Networks NIPS 2020
PAC-Bayes Analysis Beyond the Usual Bounds NIPS 2020
Doubly Stochastic Variational Inference for Neural Processes with Hierarchical Latent Variables ICML 2020
Bayesian Model Selection with Graph Structured Sparsity JMLR 2020
Primal Dual Interpretation of the Proximal Stochastic Gradient Langevin Algorithm NIPS 2020
Fast mixing of Metropolized Hamiltonian Monte Carlo: Benefits of multi-step gradients JMLR 2020
On the consistency of graph-based Bayesian semi-supervised learning and the scalability of sampling algorithms JMLR 2020
Scalable Approximate MCMC Algorithms for the Horseshoe Prior JMLR 2020
A New Class of Time Dependent Latent Factor Models with Applications JMLR 2020
Implicit Generative Modeling for Efficient Exploration ICML 2020
Quantile Graphical Models: a Bayesian Approach JMLR 2020
Expectation Propagation as a Way of Life: A Framework for Bayesian Inference on Partitioned Data JMLR 2020
Variational Label Enhancement ICML 2020
Target–Aware Bayesian Inference: How to Beat Optimal Conventional Estimators JMLR 2020