Bayesian & Probabilistic

State Space Expectation Propagation: Efficient Inference Schemes for Temporal Gaussian Processes ICML 2020 State Variable Effects in Graphical Event Models IJCAI 2020 Stationary Activations for Uncertainty Calibration in Deep Learning NeurIPS 2020 Statistical Efficiency of Thompson Sampling for Combinatorial Semi-Bandits NeurIPS 2020 Stein Variational Inference for Discrete Distributions AISTATS 2020 Stochastic Deep Gaussian Processes over Graphs NeurIPS 2020 Stochastic Differential Equations with Variational Wishart Diffusions ICML 2020 Stochastic Gradient Descent in Correlated Settings: A Study on Gaussian Processes NeurIPS 2020 Streaming Nonlinear Bayesian Tensor Decomposition UAI 2020 Structural Causal Models Are (Solvable by) Credal Networks PGM 2020 Structured Conditional Continuous Normalizing Flows for Efficient Amortized Inference in Graphical Models AISTATS 2020 Structured Probabilistic End-to-End Learning from Crowds IJCAI 2020 Structured Variational Inference in Partially Observable Unstable Gaussian Process State Space Models L4DC 2020 Structure Learning from Related Data Sets with a Hierarchical Bayesian Score PGM 2020 Strudel: Learning Structured-Decomposable Probabilistic Circuits PGM 2020 Sub-linear Regret Bounds for Bayesian Optimisation in Unknown Search Spaces NeurIPS 2020 Submodel Decomposition for Solving Limited Memory Influence Diagrams (Student Abstract) AAAI 2020 Sum-Product Network Decompilation PGM 2020 Sum-Product Networks for Robust Automatic Speaker Identification Interspeech 2020 Sum-Product-Transform Networks: Exploiting Symmetries using Invertible Transformations PGM 2020 Supervised Learning with Background Knowledge PGM 2020 SVQN: Sequential Variational Soft Q-Learning Networks ICLR 2020 Switching Poisson Gamma Dynamical Systems IJCAI 2020 Target–Aware Bayesian Inference: How to Beat Optimal Conventional Estimators JMLR 2020 Task-Agnostic Amortized Inference of Gaussian Process Hyperparameters NeurIPS 2020