Bayesian Inference

Online sampling from log-concave distributions NeurIPS 2019 On Privacy Protection of Latent Dirichlet Allocation Model Training IJCAI 2019 On Structure Priors for Learning Bayesian Networks AISTATS 2019 On Testing of Uniform Samplers AAAI 2019 On the Convergence of Gaussian Belief Propagation with Nodes of Arbitrary Size JMLR 2019 On the Hardness of Probabilistic Inference Relaxations AAAI 2019 On the Importance of the Kullback-Leibler Divergence Term in Variational Autoencoders for Text Generation EMNLP 2019 On Theory for BART AISTATS 2019 On Tractable Computation of Expected Predictions NeurIPS 2019 Open Domain Event Extraction Using Neural Latent Variable Models ACL 2019 OpenMarkov, an Open-Source Tool for Probabilistic Graphical Models IJCAI 2019 Optimal PAC-Bayesian Posteriors for Stochastic Classifiers and their use for Choice of SVM Regularization Parameter ACML 2019 Optimistic Distributionally Robust Optimization for Nonparametric Likelihood Approximation NeurIPS 2019 Orthogonality-Promoting Dictionary Learning via Bayesian Inference AAAI 2019 Overcoming Mean-Field Approximations in Recurrent Gaussian Process Models ICML 2019 PAC-Bayes under potentially heavy tails NeurIPS 2019 PAC-Bayes Un-Expected Bernstein Inequality NeurIPS 2019 Phrase Grounding by Soft-Label Chain Conditional Random Field EMNLP 2019 PointFlow: 3D Point Cloud Generation With Continuous Normalizing Flows ICCV 2019 Poisson-Minibatching for Gibbs Sampling with Convergence Rate Guarantees NeurIPS 2019 Poisson-Randomized Gamma Dynamical Systems NeurIPS 2019 PoseRBPF: A Rao-Blackwellized Particle Filter for6D Object Pose Estimation RSS 2019 Practical Deep Learning with Bayesian Principles NeurIPS 2019 Practical Multi-fidelity Bayesian Optimization for Hyperparameter Tuning UAI 2019 Practical Two-Step Lookahead Bayesian Optimization NeurIPS 2019