Bayesian Inference

Stochastic Classifiers for Unsupervised Domain Adaptation CVPR 2020 Stochastic Deep Gaussian Processes over Graphs NeurIPS 2020 Stochastic Differential Equations with Variational Wishart Diffusions ICML 2020 Stochastic Neural Network with Kronecker Flow AISTATS 2020 Stochastic Normalizing Flows NeurIPS 2020 Stochastic Particle-Optimization Sampling and the Non-Asymptotic Convergence Theory AISTATS 2020 Stochastic Stein Discrepancies NeurIPS 2020 Stopping criterion for active learning based on deterministic generalization bounds AISTATS 2020 Streamlined Variational Inference with Higher Level Random Effects JMLR 2020 STReSSD: Sim-To-Real from Sound for Stochastic Dynamics CoRL 2020 Structured Variational Inference in Partially Observable Unstable Gaussian Process State Space Models L4DC 2020 Structure Learning from Related Data Sets with a Hierarchical Bayesian Score PGM 2020 Subgroup-based Rank-1 Lattice Quasi-Monte Carlo NeurIPS 2020 SUMO: Unbiased Estimation of Log Marginal Probability for Latent Variable Models ICLR 2020 SVGD as a kernelized Wasserstein gradient flow of the chi-squared divergence NeurIPS 2020 Switching Poisson Gamma Dynamical Systems IJCAI 2020 Syn2Real Transfer Learning for Image Deraining Using Gaussian Processes CVPR 2020 Target–Aware Bayesian Inference: How to Beat Optimal Conventional Estimators JMLR 2020 Task Agnostic Robust Learning on Corrupt Outputs by Correlation-Guided Mixture Density Networks CVPR 2020 Task-Oriented Active Perception and Planning in Environments with Partially Known Semantics ICML 2020 The Boomerang Sampler ICML 2020 The Indian Chefs Process UAI 2020 The Kalai-Smorodinsky solution for many-objective Bayesian optimization JMLR 2020 The k-tied Normal Distribution: A Compact Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks ICML 2020 Theory-Based Causal Transfer:Integrating Instance-Level Induction and Abstract-Level Structure Learning AAAI 2020