Bayesian & Probabilistic

Provable Gaussian Embedding with One Observation NeurIPS 2018 Provable Variational Inference for Constrained Log-Submodular Models NeurIPS 2018 Proximal Graphical Event Models NeurIPS 2018 Proximity Variational Inference AISTATS 2018 Ranking Distributions based on Noisy Sorting ICML 2018 Rectangular Bounding Process NeurIPS 2018 Regret bounds for meta Bayesian optimization with an unknown Gaussian process prior NeurIPS 2018 Representations of Bayesian networks by low-rank models PGM 2018 Representing and Learning High Dimensional Data With the Optimal Transport Map From a Probabilistic Viewpoint CVPR 2018 Representing Social Media Users for Sarcasm Detection EMNLP 2018 Robust and Scalable Models of Microbiome Dynamics ICML 2018 Safe Active Learning for Time-Series Modeling with Gaussian Processes NeurIPS 2018 Same-Decision Probability: Threshold Robustness and Application to Explanation PGM 2018 Scalable Bayes via Barycenter in Wasserstein Space JMLR 2018 Scalable Gaussian Processes with Billions of Inducing Inputs via Tensor Train Decomposition AISTATS 2018 Scalable Generalized Dynamic Topic Models AISTATS 2018 Scalable Hyperparameter Transfer Learning NeurIPS 2018 Scalable Initial State Interdiction for Factored MDPs IJCAI 2018 Scaling Gaussian Process Regression with Derivatives NeurIPS 2018 Scaling the Poisson GLM to massive neural datasets through polynomial approximations NeurIPS 2018 Scaling up Data Augmentation MCMC via Calibration JMLR 2018 Semi-Supervised Prediction-Constrained Topic Models AISTATS 2018 Simple, Distributed, and Accelerated Probabilistic Programming NeurIPS 2018 Sparse Estimation in Ising Model via Penalized Monte Carlo Methods JMLR 2018 Sparse Learning in Gaussian Chain Graphs for State Space Models PGM 2018