Bayesian Inference

Causal Inference through a Witness Protection Program JMLR 2016 Completely random measures for modelling block-structured sparse networks NeurIPS 2016 Compressing Bayes Net CPTs with Persistent Leaky Causes PGM 2016 Coresets for Scalable Bayesian Logistic Regression NeurIPS 2016 Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning ICML 2016 Eliciting Categorical Data for Optimal Aggregation NeurIPS 2016 Estimating Causal Effects with Ancestral Graph Markov Models PGM 2016 Exact Inference on Conditional Linear Γ-Gaussian Bayesian Networks PGM 2016 Examples are not enough, learn to criticize! Criticism for Interpretability NeurIPS 2016 Finite-Dimensional BFRY Priors and Variational Bayesian Inference for Power Law Models NeurIPS 2016 Hybrid Copula Bayesian Networks PGM 2016 Inference by Reparameterization in Neural Population Codes NeurIPS 2016 Learning Parameters of Hybrid Time Bayesian Networks PGM 2016 Learning Tractable Multidimensional Bayesian Network Classifiers PGM 2016 Learning under uncertainty: a comparison between R-W and Bayesian approach NeurIPS 2016 Making Large Cox’s Proportional Hazard Models Tractable in Bayesian Networks PGM 2016 Measuring the reliability of MCMC inference with bidirectional Monte Carlo NeurIPS 2016 Multi-view Anomaly Detection via Robust Probabilistic Latent Variable Models NeurIPS 2016 On Bayesian Network Inference with Simple Propagation PGM 2016 Online Bayesian Moment Matching for Topic Modeling with Unknown Number of Topics NeurIPS 2016 On Stacking Probabilistic Temporal Models with Bidirectional Information Flow PGM 2016 Optimistic Gittins Indices NeurIPS 2016 Probing the Compositionality of Intuitive Functions NeurIPS 2016 Regime Aware Learning PGM 2016 Regression Methods Applied to Flight Variables for Situational Awareness Estimation Using Dynamic Bayesian Networks PGM 2016