Bayesian & Probabilistic

Learning Models from Data with Measurement Error: Tackling Underreporting ICML 2019 Learning Models of Sequential Decision-Making with Partial Specification of Agent Behavior AAAI 2019 Learning Plackett-Luce Mixtures from Partial Preferences AAAI 2019 Learning Reward Machines for Partially Observable Reinforcement Learning NeurIPS 2019 Learning Structured Decision Problems with Unawareness ICML 2019 Learning to Address Health Inequality in the United States with a Bayesian Decision Network AAAI 2019 Learning Tree Structures from Noisy Data AISTATS 2019 Leveraging Structural and Semantic Correspondence for Attribute-Oriented Aspect Sentiment Discovery EMNLP 2019 LF-MMI Training of Bayesian and Gaussian Process Time Delay Neural Networks for Speech Recognition Interspeech 2019 Lifted Hinge-Loss Markov Random Fields AAAI 2019 Lifted Message Passing for Hybrid Probabilistic Inference IJCAI 2019 Lifted Weight Learning of Markov Logic Networks Revisited AISTATS 2019 Locally Private Bayesian Inference for Count Models ICML 2019 Low-Complexity Nonparametric Bayesian Online Prediction with Universal Guarantees NeurIPS 2019 LR-GLM: High-Dimensional Bayesian Inference Using Low-Rank Data Approximations ICML 2019 Marginal Inference in Continuous Markov Random Fields Using Mixtures AAAI 2019 Markov Properties of Discrete Determinantal Point Processes AISTATS 2019 Markov Random Fields for Collaborative Filtering NeurIPS 2019 Matched Bipartite Block Model with Covariates JMLR 2019 Maximum a posteriori Speech Enhancement Based on Double Spectrum Interspeech 2019 Maximum Likelihood for Gaussian Process Classification and Generalized Linear Mixed Models under Case-Control Sampling JMLR 2019 Max-MIG: an Information Theoretic Approach for Joint Learning from Crowds ICLR 2019 Meimei: An Efficient Probabilistic Approach for Semantically Annotating Tables AAAI 2019 Meta-Learning Probabilistic Inference for Prediction ICLR 2019 Meta Learning with Relational Information for Short Sequences NeurIPS 2019