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Bayesian Learning

1663 directly classified papers

Papers per year

Papers

Gaussian Process Conditional Density Estimation NIPS 2018
Constructing Deep Neural Networks by Bayesian Network Structure Learning NIPS 2018
Minimal I-MAP MCMC for Scalable Structure Discovery in Causal DAG Models ICML 2018
Nonparametric learning from Bayesian models with randomized objective functions NIPS 2018
SLANG: Fast Structured Covariance Approximations for Bayesian Deep Learning with Natural Gradient NIPS 2018
Learning Invariances using the Marginal Likelihood NIPS 2018
Adversarial Distillation of Bayesian Neural Network Posteriors ICML 2018
Bayesian Uncertainty Estimation for Batch Normalized Deep Networks ICML 2018
Markov Modulated Gaussian Cox Processes for Semi-Stationary Intensity Modeling of Events Data ICML 2018
Bayesian Coreset Construction via Greedy Iterative Geodesic Ascent ICML 2018
Bandit Learning with Implicit Feedback NIPS 2018
Thermostat-assisted continuously-tempered Hamiltonian Monte Carlo for Bayesian learning NIPS 2018
The promises and pitfalls of Stochastic Gradient Langevin Dynamics NIPS 2018
Predictive Uncertainty Estimation via Prior Networks NIPS 2018
Multiphase MCMC Sampling for Parameter Inference in Nonlinear Ordinary Differential Equations AISTATS 2018
The Binary Space Partitioning-Tree Process AISTATS 2018
Efficient Bayesian Methods for Counting Processes in Partially Observable Environments AISTATS 2018
Scalable approximate Bayesian inference for particle tracking data ICML 2018
A Symbolic Approach to Explaining Bayesian Network Classifiers IJCAI 2018
Bayesian Active Edge Evaluation on Expensive Graphs IJCAI 2018
Predictive Approximate Bayesian Computation via Saddle Points NIPS 2018
Demystifying excessively volatile human learning: A Bayesian persistent prior and a neural approximation NIPS 2018
Bayesian Inference of Temporal Task Specifications from Demonstrations NIPS 2018
Random Feature Stein Discrepancies NIPS 2018
Permuted and Augmented Stick-Breaking Bayesian Multinomial Regression JMLR 2018