Bayesian & Probabilistic

Relative gradient optimization of the Jacobian term in unsupervised deep learning NeurIPS 2020 Residual Neural Processes AAAI 2020 Residual Sum-Product Networks PGM 2020 Revisiting the Sample Complexity of Sparse Spectrum Approximation of Gaussian Processes NeurIPS 2020 Robust, Accurate Stochastic Optimization for Variational Inference NeurIPS 2020 Robust Data Programming with Precision-guided Labeling Functions AAAI 2020 Robustness Computation of Dynamic Controllability in Probabilistic Temporal Networks with Ordinary Distributions IJCAI 2020 Robustness of Bayesian Neural Networks to Gradient-Based Attacks NeurIPS 2020 Sample Complexity of Kalman Filtering for Unknown Systems L4DC 2020 Sampling from a k-DPP without looking at all items NeurIPS 2020 Scalable and Flexible Clustering of Grouped Data via Parallel and Distributed Sampling in Versatile Hierarchical Dirichlet Processes UAI 2020 Scalable Approximate MCMC Algorithms for the Horseshoe Prior JMLR 2020 Scalable Bayesian Network Structure Learning via Maximum Acyclic Subgraph PGM 2020 Scalable Gaussian Process Regression Networks IJCAI 2020 Scalable Probabilistic Matrix Factorization with Graph-Based Priors AAAI 2020 Scalable Uncertainty for Computer Vision With Functional Variational Inference CVPR 2020 Scalable Variational Bayesian Kernel Selection for Sparse Gaussian Process Regression AAAI 2020 Scaling Hidden Markov Language Models EMNLP 2020 Scaling Up AND/OR Abstraction Sampling IJCAI 2020 Self-Attentive Hawkes Process ICML 2020 Semi-parametric Learning of Structured Temporal Point Processes JMLR 2020 Sensor Placement for Spatial Gaussian Processes with Integral Observations UAI 2020 Simultaneous Inference for Pairwise Graphical Models with Generalized Score Matching JMLR 2020 Slice Sampling for General Completely Random Measures UAI 2020 Smooth And Consistent Probabilistic Regression Trees NeurIPS 2020