Bayesian & Probabilistic

The Functional Neural Process NeurIPS 2019 The Incomplete Rosetta Stone problem: Identifiability results for Multi-view Nonlinear ICA UAI 2019 The LORACs Prior for VAEs: Letting the Trees Speak for the Data AISTATS 2019 Theoretical guarantees for sampling and inference in generative models with latent diffusions COLT 2019 The pywmi Framework and Toolbox for Probabilistic Inference using Weighted Model Integration IJCAI 2019 The Randomized Midpoint Method for Log-Concave Sampling NeurIPS 2019 TopicEq: A Joint Topic and Mathematical Equation Model for Scientific Texts AAAI 2019 Topic Modeling with Wasserstein Autoencoders ACL 2019 Toward a Characterization of Loss Functions for Distribution Learning NeurIPS 2019 Towards Hardware-Aware Tractable Learning of Probabilistic Models NeurIPS 2019 Traffic Updates: Saying a Lot While Revealing a Little AAAI 2019 Training Variational Autoencoders with Buffered Stochastic Variational Inference AISTATS 2019 Tree-Structured Recurrent Switching Linear Dynamical Systems for Multi-Scale Modeling ICLR 2019 Trust Region Sequential Variational Inference ACML 2019 Unbiased Implicit Variational Inference AISTATS 2019 Uncertainty on Asynchronous Time Event Prediction NeurIPS 2019 Uncover the Ground-Truth Relations in Distant Supervision: A Neural Expectation-Maximization Framework EMNLP 2019 Understanding Composition of Word Embeddings via Tensor Decomposition ICLR 2019 uniblock: Scoring and Filtering Corpus with Unicode Block Information EMNLP 2019 Uniform Error Bounds for Gaussian Process Regression with Application to Safe Control NeurIPS 2019 Universal Boosting Variational Inference NeurIPS 2019 Unsupervised Discovery of Gendered Language through Latent-Variable Modeling ACL 2019 Unsupervised Fake News Detection on Social Media: A Generative Approach AAAI 2019 Unsupervised Learning with Contrastive Latent Variable Models AAAI 2019 Unsupervised Primitive Discovery for Improved 3D Generative Modeling CVPR 2019