Bayesian & Probabilistic

Learning from Stochastically Revealed Preference NeurIPS 2022 Learning General Gaussian Mixture Model with Integral Cosine Similarity IJCAI 2022 Learning GMMs with Nearly Optimal Robustness Guarantees COLT 2022 Learning Inconsistent Preferences with Gaussian Processes AISTATS 2022 Learning interacting dynamical systems with latent Gaussian process ODEs NeurIPS 2022 Learning Large DAGs by Combining Continuous Optimization and Feedback Arc Set Heuristics AAAI 2022 Learning Manifold Dimensions with Conditional Variational Autoencoders NeurIPS 2022 Learning Noisy-Or Networks with an Application in Linguistics PGM 2022 Learning POMDP Models with Similarity Space Regularization: a Linear Gaussian Case Study L4DC 2022 Learning Sparse Fixed-Structure Gaussian Bayesian Networks AISTATS 2022 Learning Temporal Evolution of Spatial Dependence with Generalized Spatiotemporal Gaussian Process Models JMLR 2022 Learning Tractable Probabilistic Models from Inconsistent Local Estimates NeurIPS 2022 Lifted Division for Lifted Hugin Belief Propagation AISTATS 2022 Limited Memory Influence Diagrams for Attribute Statistical Process Control with Variable Sample Sizes PGM 2022 Local Bayesian optimization via maximizing probability of descent NeurIPS 2022 Local-Global MCMC kernels: the best of both worlds NeurIPS 2022 Local Latent Space Bayesian Optimization over Structured Inputs NeurIPS 2022 Logical Credal Networks NeurIPS 2022 Log-Linear-Time Gaussian Processes Using Binary Tree Kernels NeurIPS 2022 Look-Ahead Acquisition Functions for Bernoulli Level Set Estimation AISTATS 2022 Mappings for Marginal Probabilities with Applications to Models in Statistical Physics JMLR 2022 Marginal Tail-Adaptive Normalizing Flows ICML 2022 Markov Chain Score Ascent: A Unifying Framework of Variational Inference with Markovian Gradients NeurIPS 2022 Measuring the robustness of Gaussian processes to kernel choice AISTATS 2022 Metalearning Linear Bandits by Prior Update AISTATS 2022