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Bayesian Learning

1663 directly classified papers

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Papers

Task-Agnostic Amortized Inference of Gaussian Process Hyperparameters NIPS 2020
Constraing-Based Learning for Continous-Time Bayesian Networks PGM 2020
Ensembling geophysical models with Bayesian Neural Networks NIPS 2020
When and How to Lift the Lockdown? Global COVID-19 Scenario Analysis and Policy Assessment using Compartmental Gaussian Processes NIPS 2020
SDE-Net: Equipping Deep Neural Networks with Uncertainty Estimates ICML 2020
Being Bayesian, Even Just a Bit, Fixes Overconfidence in ReLU Networks ICML 2020
How Good is the Bayes Posterior in Deep Neural Networks Really? ICML 2020
Towards a Hierarchical Bayesian Model of Multi-View Anomaly Detection IJCAI 2020
Projected Stein Variational Gradient Descent NIPS 2020
A Bayesian Nonparametrics View into Deep Representations NIPS 2020
Bayesian Causal Structural Learning with Zero-Inflated Poisson Bayesian Networks NIPS 2020
The k-tied Normal Distribution: A Compact Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks ICML 2020
Bayesian Sparsification of Deep C-valued Networks ICML 2020
Bayesian Graph Neural Networks with Adaptive Connection Sampling ICML 2020
Algorithmic recourse under imperfect causal knowledge: a probabilistic approach NIPS 2020
Reconsidering Generative Objectives For Counterfactual Reasoning NIPS 2020
Generalised Bayesian Filtering via Sequential Monte Carlo NIPS 2020
BINOCULARS for efficient, nonmyopic sequential experimental design ICML 2020
Information Particle Filter Tree: An Online Algorithm for POMDPs with Belief-Based Rewards on Continuous Domains ICML 2020
Generating Well-Formed Answers by Machine Reading with Stochastic Selector Networks AAAI 2020
Safe Imitation Learning via Fast Bayesian Reward Inference from Preferences ICML 2020
Bayesian Adversarial Human Motion Synthesis CVPR 2020
Scalable Uncertainty for Computer Vision With Functional Variational Inference CVPR 2020
Few-shot Relation Extraction via Bayesian Meta-learning on Relation Graphs ICML 2020
Quantum Probabilistic Models Using Feynman Diagram Rules for Better Understanding the Information Diffusion Dynamics in Online Social Networks AAAI 2020