Probabilistic Modeling

RIJP at SemEval-2020 Task 1: Gaussian-based Embeddings for Semantic Change Detection SemEval 2020 Sampling-Free Learning of Bayesian Quantized Neural Networks ICLR 2020 Scalable Belief Propagation via Relaxed Scheduling NeurIPS 2020 Scalable Exact Inference in Multi-Output Gaussian Processes ICML 2020 Scalable Gaussian Process Regression Networks IJCAI 2020 Scalable Gaussian Process Separation for Kernels with a Non-Stationary Phase ICML 2020 Scalable Inference on the Soft Affiliation Graph Model for Overlapping Community Detection ACML 2020 Scalable Nonparametric Factorization for High-Order Interaction Events AISTATS 2020 Scalable Probabilistic Matrix Factorization with Graph-Based Priors AAAI 2020 Scaling Up AND/OR Abstraction Sampling IJCAI 2020 Scaling up Hybrid Probabilistic Inference with Logical and Arithmetic Constraints via Message Passing ICML 2020 Self-Modulating Nonparametric Event-Tensor Factorization ICML 2020 Semi-Modular Inference: enhanced learning in multi-modular models by tempering the influence of components AISTATS 2020 Semi-parametric Learning of Structured Temporal Point Processes JMLR 2020 Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness NeurIPS 2020 Slice Sampling for General Completely Random Measures UAI 2020 Solving Multiple Inference by Minimizing Expected Loss PGM 2020 Sparse Gaussian Processes with Spherical Harmonic Features ICML 2020 Spectral Subsampling MCMC for Stationary Time Series ICML 2020 Spike and slab variational Bayes for high dimensional logistic regression NeurIPS 2020 State Space Expectation Propagation: Efficient Inference Schemes for Temporal Gaussian Processes ICML 2020 State Variable Effects in Graphical Event Models IJCAI 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