Bayesian & Probabilistic

Nonconvex Sparse Graph Learning under Laplacian Constrained Graphical Model NeurIPS 2020 Non-exchangeable feature allocation models with sublinear growth of the feature sizes AISTATS 2020 Non-Parametric Calibration for Classification AISTATS 2020 Nonparametric Fisher Geometry with Application to Density Estimation UAI 2020 Nonparametric graphical model for counts JMLR 2020 Non-reversible Gaussian processes for identifying latent dynamical structure in neural data NeurIPS 2020 ODIN: ODE-Informed Regression for Parameter and State Inference in Time-Continuous Dynamical Systems AAAI 2020 On a possibility of gradual model-learning PGM 2020 On Application of Bayesian Parametric and Non-parametric Methods for User Cohorting in Product Search ACL 2020 On Infinite-Width Hypernetworks NeurIPS 2020 Online Neural Connectivity Estimation with Noisy Group Testing NeurIPS 2020 On Relativistic f-Divergences ICML 2020 On Simulation and Trajectory Prediction with Gaussian Process Dynamics L4DC 2020 On the Anatomy of MCMC-Based Maximum Likelihood Learning of Energy-Based Models AAAI 2020 On the Encoder-Decoder Incompatibility in Variational Text Modeling and Beyond ACL 2020 On the Expressiveness of Approximate Inference in Bayesian Neural Networks NeurIPS 2020 On the Relationship Between Probabilistic Circuits and Determinantal Point Processes UAI 2020 On the Theoretical Guarantees for Parameter Estimation of Gaussian Random Field Models: A Sparse Precision Matrix Approach JMLR 2020 Optimal Variance Control of the Score-Function Gradient Estimator for Importance-Weighted Bounds NeurIPS 2020 Ordering Variables for Weighted Model Integration UAI 2020 Parallel AND/OR Search for Marginal MAP AAAI 2020 Parametric Gaussian Process Regressors ICML 2020 Particle Filter Recurrent Neural Networks AAAI 2020 Penalized Langevin dynamics with vanishing penalty for smooth and log-concave targets NeurIPS 2020 PGM_PyLib: A Toolkit for Probabilistic Graphical Models in Python PGM 2020