Bayesian Inference

Variational Bayesian Multiple Instance Learning With Gaussian Processes CVPR 2017 Variational Boosting: Iteratively Refining Posterior Approximations ICML 2017 Variational Dropout Sparsifies Deep Neural Networks ICML 2017 Variational Inference for Gaussian Process Models with Linear Complexity NeurIPS 2017 Variational Inference for Sparse and Undirected Models ICML 2017 Variational Inference via χ Upper Bound Minimization NeurIPS 2017 Variational Mixtures of Gaussian Processes for Classification IJCAI 2017 Variational Particle Approximations JMLR 2017 Variational Policy for Guiding Point Processes ICML 2017 Variational Recurrent Neural Networks for Speech Separation Interspeech 2017 Visual-Inertial Odometry on Chip: An Algorithm-and-Hardware Co-design Approach RSS 2017 What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision? NeurIPS 2017 Why is Posterior Sampling Better than Optimism for Reinforcement Learning? ICML 2017 YASS: Yet Another Spike Sorter NeurIPS 2017 A Bayesian method for reducing bias in neural representational similarity analysis NeurIPS 2016 A Bayesian model for joint word alignment and part-of-speech transfer COLING 2016 A Bayesian Nonparametric Approach for Estimating Individualized Treatment-Response Curves MLHC 2016 A Bounded p-norm Approximation of Max-Convolution for Sub-Quadratic Bayesian Inference on Additive Factors JMLR 2016 A Column Generation Bound Minimization Approach with PAC-Bayesian Generalization Guarantees AISTATS 2016 Adapting Multicomponent Predictive Systems using Hybrid Adaptation Strategies with Auto-WEKA in Process Industry AutoML 2016 A Field Model for Repairing 3D Shapes CVPR 2016 A General Framework for Constrained Bayesian Optimization using Information-based Search JMLR 2016 A Gibbs Sampler for Learning DAGs JMLR 2016 A Kernelized Stein Discrepancy for Goodness-of-fit Tests ICML 2016 A Kernel Test of Goodness of Fit ICML 2016