Bayesian & Probabilistic

Improving Local Identifiability in Probabilistic Box Embeddings NeurIPS 2020 Incorporating Interpretable Output Constraints in Bayesian Neural Networks NeurIPS 2020 Infinite ShapeOdds: Nonparametric Bayesian Models for Shape Representations AAAI 2020 Information Particle Filter Tree: An Online Algorithm for POMDPs with Belief-Based Rewards on Continuous Domains ICML 2020 Information Theoretic Optimal Learning of Gaussian Graphical Models COLT 2020 Inter-domain Deep Gaussian Processes ICML 2020 Interpretable Models for Understanding Immersive Simulations IJCAI 2020 Introducing Routing Uncertainty in Capsule Networks NeurIPS 2020 Invertible Gaussian Reparameterization: Revisiting the Gumbel-Softmax NeurIPS 2020 Investigating Matureness of Probabilistic Graphical Models for Dry-Bulk Shipping PGM 2020 Iterative Repair of Social Robot Programs from Implicit User Feedback via Bayesian Inference RSS 2020 Joints in Random Forests NeurIPS 2020 Kernel Conditional Density Operators AISTATS 2020 Knowing-How under Uncertainty (Extended Abstract) IJCAI 2020 Label Error Correction and Generation through Label Relationships AAAI 2020 Latent Dynamic Factor Analysis of High-Dimensional Neural Recordings NeurIPS 2020 Latent Geographical Factors for Analyzing the Evolution of Dialects in Contact EMNLP 2020 Latent Simplex Position Model: High Dimensional Multi-view Clustering with Uncertainty Quantification JMLR 2020 Latent Variable Modelling with Hyperbolic Normalizing Flows ICML 2020 Layering-MCMC for Structure Learning in Bayesian Networks UAI 2020 Learnable Bernoulli Dropout for Bayesian Deep Learning AISTATS 2020 Learning Bayesian Networks Under Sparsity Constraints: A Parameterized Complexity Analysis IJCAI 2020 Learning Bayesian Networks with Cops and Robbers PGM 2020 Learning Big Gaussian Bayesian Networks: Partition, Estimation and Fusion JMLR 2020 Learning Constrained Dynamics with Gauss’ Principle adhering Gaussian Processes L4DC 2020