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Bayesian Learning

1663 directly classified papers

Papers per year

Papers

A Probabilistic Programming Approach To Probabilistic Data Analysis NIPS 2016
Bayesian latent structure discovery from multi-neuron recordings NIPS 2016
Batched Gaussian Process Bandit Optimization via Determinantal Point Processes NIPS 2016
Differential Privacy without Sensitivity NIPS 2016
Kernel Bayesian Inference with Posterior Regularization NIPS 2016
Learning Treewidth-Bounded Bayesian Networks with Thousands of Variables NIPS 2016
Bayesian Optimization with Robust Bayesian Neural Networks NIPS 2016
The Nonparametric Kernel Bayes Smoother AISTATS 2016
Sequential Inference for Deep Gaussian Process AISTATS 2016
Non-Stationary Gaussian Process Regression with Hamiltonian Monte Carlo AISTATS 2016
Provable Bayesian Inference via Particle Mirror Descent AISTATS 2016
Unbounded Bayesian Optimization via Regularization AISTATS 2016
BayesPy: Variational Bayesian Inference in Python JMLR 2016
Regret Analysis of the Finite-Horizon Gittins Index Strategy for Multi-Armed Bandits COLT 2016
Scalable Structure Discovery in Regression using Gaussian Processes AUTOML 2016
Confusions over Time: An Interpretable Bayesian Model to Characterize Trends in Decision Making NIPS 2016
Particle Gibbs for Infinite Hidden Markov Models NIPS 2015
Bayesian Optimization with Exponential Convergence NIPS 2015
Copula variational inference NIPS 2015
Learning spatiotemporal trajectories from manifold-valued longitudinal data NIPS 2015
Manifold-valued Dirichlet Processes ICML 2015
A hybrid sampler for Poisson-Kingman mixture models NIPS 2015
Frank-Wolfe Bayesian Quadrature: Probabilistic Integration with Theoretical Guarantees NIPS 2015
Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks ICML 2015
A trust-region method for stochastic variational inference with applications to streaming data ICML 2015