Bayesian Inference

Cross-Corpora Unsupervised Learning of Trajectories in Autism Spectrum Disorders JMLR 2016 Data-driven learning of symbolic constraints for a log-linear model in a phonological setting COLING 2016 Deep Gaussian Processes for Regression using Approximate Expectation Propagation ICML 2016 Determinantal Regularization for Ensemble Variable Selection AISTATS 2016 Dirichlet Process Mixture Model for Correcting Technical Variation in Single-Cell Gene Expression Data ICML 2016 DISCO Nets : DISsimilarity COefficients Networks NeurIPS 2016 Distributed Flexible Nonlinear Tensor Factorization NeurIPS 2016 Diversity-Promoting Bayesian Learning of Latent Variable Models ICML 2016 Doubly Decomposing Nonparametric Tensor Regression ICML 2016 DR-ABC: Approximate Bayesian Computation with Kernel-Based Distribution Regression ICML 2016 Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning ICML 2016 d-VMP: Distributed Variational Message Passing PGM 2016 Early Stopping as Nonparametric Variational Inference AISTATS 2016 Electronic Health Record Analysis via Deep Poisson Factor Models JMLR 2016 Eliciting Categorical Data for Optimal Aggregation NeurIPS 2016 Ensuring Rapid Mixing and Low Bias for Asynchronous Gibbs Sampling ICML 2016 Enumerating Equivalence Classes of Bayesian Networks using EC Graphs AISTATS 2016 Estimating Accuracy from Unlabeled Data: A Bayesian Approach ICML 2016 Estimating Causal Effects with Ancestral Graph Markov Models PGM 2016 Estimating Nonlinear Neural Response Functions using GP Priors and Kronecker Methods NeurIPS 2016 Exact Inference on Conditional Linear Γ-Gaussian Bayesian Networks PGM 2016 Examples are not enough, learn to criticize! Criticism for Interpretability NeurIPS 2016 Exploration of the (Non-)Asymptotic Bias and Variance of Stochastic Gradient Langevin Dynamics JMLR 2016 Exponential Stochastic Cellular Automata for Massively Parallel Inference AISTATS 2016 Extended and Unscented Kitchen Sinks ICML 2016