Bayesian Inference

Automatic Reparameterisation of Probabilistic Programs ICML 2020 A Variational Approach for Learning from Positive and Unlabeled Data NeurIPS 2020 A Variational Perturbative Approach to Planning in Graph-Based Markov Decision Processes AAAI 2020 BaKer-Nets: Bayesian Random Kernel Mapping Networks IJCAI 2020 Bandit Algorithms Based on Thompson Sampling for Bounded Reward Distributions ALT 2020 Bandit optimisation of functions in the Matérn kernel RKHS AISTATS 2020 Batch simulations and uncertainty quantification in Gaussian process surrogate approximate Bayesian computation UAI 2020 Bayes-Adaptive Monte-Carlo Planning and Learning for Goal-Oriented Dialogues AAAI 2020 Bayes-enhanced Lifelong Attention Networks for Sentiment Classification COLING 2020 Bayesian Adversarial Attack on Graph Neural Networks (Student Abstract) AAAI 2020 Bayesian Adversarial Human Motion Synthesis CVPR 2020 Bayesian Attention Modules NeurIPS 2020 Bayesian Bits: Unifying Quantization and Pruning NeurIPS 2020 Bayesian Causal Structural Learning with Zero-Inflated Poisson Bayesian Networks NeurIPS 2020 Bayesian Closed Surface Fitting Through Tensor Products JMLR 2020 Bayesian Decision Process for Budget-efficient Crowdsourced Clustering IJCAI 2020 Bayesian Deep Ensembles via the Neural Tangent Kernel NeurIPS 2020 Bayesian Deep Learning and a Probabilistic Perspective of Generalization NeurIPS 2020 Bayesian Differential Privacy for Machine Learning ICML 2020 Bayesian Experimental Design for Implicit Models by Mutual Information Neural Estimation ICML 2020 Bayesian experimental design using regularized determinantal point processes AISTATS 2020 Bayesian filtering unifies adaptive and non-adaptive neural network optimization methods NeurIPS 2020 Bayesian Graph Neural Networks with Adaptive Connection Sampling ICML 2020 Bayesian Image Classification with Deep Convolutional Gaussian Processes AISTATS 2020 Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances ICML 2020