Bayesian Learning

Probabilistic Line Searches for Stochastic Optimization JMLR 2017 Quantifying Aspect Bias in Ordinal Ratings using a Bayesian Approach IJCAI 2017 Robust Probabilistic Modeling with Bayesian Data Reweighting ICML 2017 Scalable Log Determinants for Gaussian Process Kernel Learning NeurIPS 2017 Scalable Model Selection for Belief Networks NeurIPS 2017 Scalable Multi-Class Gaussian Process Classification using Expectation Propagation ICML 2017 ShapeOdds: Variational Bayesian Learning of Generative Shape Models CVPR 2017 Soft-Bayes: Prod for Mixtures of Experts with Log-Loss ALT 2017 Strong Syntax Splitting for Iterated Belief Revision IJCAI 2017 Structured Bayesian Pruning via Log-Normal Multiplicative Noise NeurIPS 2017 Student-t Process Regression with Student-t Likelihood IJCAI 2017 Thompson Sampling for Linear-Quadratic Control Problems AISTATS 2017 Thompson Sampling for the MNL-Bandit COLT 2017 Time for a Change: a Tutorial for Comparing Multiple Classifiers Through Bayesian Analysis JMLR 2017 Unified Representation and Lifted Sampling for Generative Models of Social Networks IJCAI 2017 Variational Mixtures of Gaussian Processes for Classification IJCAI 2017 A Genetic Algorithm for Learning Parameters in Bayesian Networks using Expectation Maximization PGM 2016 A Gibbs Sampler for Learning DAGs JMLR 2016 Analysis of Variational Bayesian Factorizations for Sparse and Low-Rank Estimation ICML 2016 An Empirical-Bayes Score for Discrete Bayesian Networks PGM 2016 An Information-Theoretic Analysis of Thompson Sampling JMLR 2016 A Probabilistic Programming Approach To Probabilistic Data Analysis NeurIPS 2016 Assessing Idiosyncrasies in a Bayesian Model of Speech Communication Interspeech 2016 Batched Gaussian Process Bandit Optimization via Determinantal Point Processes NeurIPS 2016 Bayesian Decision Process for Cost-Efficient Dynamic Ranking via Crowdsourcing JMLR 2016