Probabilistic Modeling

A Unified Statistically Efficient Estimation Framework for Unnormalized Models AISTATS 2020 Automatic Reparameterisation of Probabilistic Programs ICML 2020 Autoregressive Score Matching NeurIPS 2020 A Variational Perturbative Approach to Planning in Graph-Based Markov Decision Processes AAAI 2020 A Variational Point Process Model for Social Event Sequences AAAI 2020 Axioms for Learning from Pairwise Comparisons NeurIPS 2020 Bayesian Experimental Design for Implicit Models by Mutual Information Neural Estimation ICML 2020 Bayesian Learning of Probabilistic Dipole Inversion for Quantitative Susceptibility Mapping MIDL 2020 Bean Machine: A Declarative Probabilistic Programming Language For Efficient Programmable Inference PGM 2020 Belief Merging Operators as Maximum Likelihood Estimators IJCAI 2020 Beyond the Grounding Bottleneck: Datalog Techniques for Inference in Probabilistic Logic Programs AAAI 2020 Beyond the Mean-Field: Structured Deep Gaussian Processes Improve the Predictive Uncertainties NeurIPS 2020 Beyond Trees: Analysis and Convergence of Belief Propagation in Graphs with Multiple Cycles AAAI 2020 Bi-level Probabilistic Feature Learning for Deformable Image Registration IJCAI 2020 Bootstrapping neural processes NeurIPS 2020 Bundle Adjustment on a Graph Processor CVPR 2020 CAUSE: Learning Granger Causality from Event Sequences using Attribution Methods ICML 2020 Complex Markov Logic Networks: Expressivity and Liftability UAI 2020 Conditional Gaussian Distribution Learning for Open Set Recognition CVPR 2020 Conditional Sum-Product Networks: Imposing Structure on Deep Probabilistic Architectures PGM 2020 Confidence Sets and Hypothesis Testing in a Likelihood-Free Inference Setting ICML 2020 Constructing a Chain Event Graph from a Staged Tree PGM 2020 Continual Deep Learning by Functional Regularisation of Memorable Past NeurIPS 2020 Continual Learning with Bayesian Neural Networks for Non-Stationary Data ICLR 2020 Continuous Time Bayesian Networks with Clocks ICML 2020