Bayesian & Probabilistic

Non-linear process convolutions for multi-output Gaussian processes AISTATS 2019 Nonparametric Bayesian Aggregation for Massive Data JMLR 2019 Nonparametric Bayesian Deep Networks with Local Competition ICML 2019 Nonparametric Density Estimation & Convergence Rates for GANs under Besov IPM Losses NeurIPS 2019 No-Regret Learning in Unknown Games with Correlated Payoffs NeurIPS 2019 Offline Contextual Bayesian Optimization NeurIPS 2019 On Constrained Open-World Probabilistic Databases IJCAI 2019 One-Shot Marginal MAP Inference in Markov Random Fields UAI 2019 On Fenchel Mini-Max Learning NeurIPS 2019 On Lifted Inference Using Neural Embeddings AAAI 2019 Online Probabilistic Goal Recognition over Nominal Models IJCAI 2019 On Testing of Uniform Samplers AAAI 2019 On the Convergence of Gaussian Belief Propagation with Nodes of Arbitrary Size JMLR 2019 On the Hardness of Probabilistic Inference Relaxations AAAI 2019 On the Performance of Thompson Sampling on Logistic Bandits COLT 2019 On Tractable Computation of Expected Predictions NeurIPS 2019 Open Event Extraction from Online Text using a Generative Adversarial Network EMNLP 2019 OpenMarkov, an Open-Source Tool for Probabilistic Graphical Models IJCAI 2019 Optimal Continuous DR-Submodular Maximization and Applications to Provable Mean Field Inference ICML 2019 Optimistic Distributionally Robust Optimization for Nonparametric Likelihood Approximation NeurIPS 2019 Parameter elimination in particle Gibbs sampling NeurIPS 2019 Partially Exchangeable Networks and Architectures for Learning Summary Statistics in Approximate Bayesian Computation ICML 2019 Particle Flow Bayes’ Rule ICML 2019 Phenotype Inference with Semi-Supervised Mixed Membership Models MLHC 2019 Poisson-Randomized Gamma Dynamical Systems NeurIPS 2019