Bayesian & Probabilistic

Localizing and Amortizing: Efficient Inference for Gaussian Processes ACML 2020 Logsmooth Gradient Concentration and Tighter Runtimes for Metropolized Hamiltonian Monte Carlo COLT 2020 Low-dimensional statistical manifold embedding of directed graphs ICLR 2020 Lower Bounds for Testing Graphical Models: Colorings and Antiferromagnetic Ising Models JMLR 2020 Markovian Score Climbing: Variational Inference with KL(p||q) NeurIPS 2020 Matérn Gaussian Processes on Riemannian Manifolds NeurIPS 2020 McFlow: Monte Carlo Flow Models for Data Imputation CVPR 2020 Meta-Amortized Variational Inference and Learning AAAI 2020 Meta-Learning with Shared Amortized Variational Inference ICML 2020 Missing Values in Multiple Joint Inference of Gaussian Graphical Models PGM 2020 Mixed Hamiltonian Monte Carlo for Mixed Discrete and Continuous Variables NeurIPS 2020 Mixture Dense Regression for Object Detection and Human Pose Estimation CVPR 2020 Multi-Fidelity Bayesian Optimization via Deep Neural Networks NeurIPS 2020 Multi-Fidelity Multi-Objective Bayesian Optimization: An Output Space Entropy Search Approach AAAI 2020 Multi-Speaker Text-to-Speech Synthesis Using Deep Gaussian Processes Interspeech 2020 Multi-task Causal Learning with Gaussian Processes NeurIPS 2020 Neural Architecture Generator Optimization NeurIPS 2020 Neural Belief Reasoner IJCAI 2020 Neural Decomposition: Functional ANOVA with Variational Autoencoders AISTATS 2020 Neural Dialogue State Tracking with Temporally Expressive Networks EMNLP 2020 Neural Gibbs Sampling for Joint Event Argument Extraction AACL 2020 Neural Mixed Counting Models for Dispersed Topic Discovery ACL 2020 Neuronal Gaussian Process Regression NeurIPS 2020 New Off-Board Solution for Predicting Vehicles' Intentions in the Highway On-Ramp Using Probabilistic Classifiers (Student Abstract) AAAI 2020 Noise-Contrastive Estimation for Multivariate Point Processes NeurIPS 2020