Bayesian Inference

Simple Bayesian Algorithms for Best Arm Identification COLT 2016 Simulation and Calibration of a Fully Bayesian Marked Multidimensional Hawkes Process with Dissimilar Decays ACML 2016 Situated Language Understanding with Human-like and Visualization-Based Transparency RSS 2016 Slice Sampling on Hamiltonian Trajectories ICML 2016 Solving Marginal MAP Problems with NP Oracles and Parity Constraints NeurIPS 2016 Statistical Matching of Discrete Data by Bayesian Networks PGM 2016 Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm NeurIPS 2016 Stochastic Gradient Geodesic MCMC Methods NeurIPS 2016 Stochastic Gradient MCMC with Stale Gradients NeurIPS 2016 Stochastic Gradient Richardson-Romberg Markov Chain Monte Carlo NeurIPS 2016 Stochastic Neural Networks with Monotonic Activation Functions AISTATS 2016 Stochastic Quasi-Newton Langevin Monte Carlo ICML 2016 Stochastic Variational Inference for the HDP-HMM AISTATS 2016 String and Membrane Gaussian Processes JMLR 2016 Structured and Efficient Variational Deep Learning with Matrix Gaussian Posteriors ICML 2016 Structure Discovery in Bayesian Networks by Sampling Partial Orders JMLR 2016 Structure Learning in Bayesian Networks of a Moderate Size by Efficient Sampling JMLR 2016 Synthesis of MCMC and Belief Propagation NeurIPS 2016 The Effect of Combination Functions on the Complexity of Relational Bayesian Networks PGM 2016 The Forget-me-not Process NeurIPS 2016 The Generalized Reparameterization Gradient NeurIPS 2016 The Knowledge Gradient for Sequential Decision Making with Stochastic Binary Feedbacks ICML 2016 The Nonparametric Kernel Bayes Smoother AISTATS 2016 The Parallel Knowledge Gradient Method for Batch Bayesian Optimization NeurIPS 2016 The Parameterized Complexity of Approximate Inference in Bayesian Networks PGM 2016