Bayesian & Probabilistic

Learning Bayesian Networks from Ordinal Data JMLR 2021 Learning Deeper Non-Monotonic Networks by Softly Transferring Solution Space IJCAI 2021 Learning Density Distribution of Reachable States for Autonomous Systems CoRL 2021 Learning Energy-Based Model with Variational Auto-Encoder as Amortized Sampler AAAI 2021 Learning Fast-Inference Bayesian Networks NeurIPS 2021 Learning GPLVM with arbitrary kernels using the unscented transformation AISTATS 2021 Learning Nonparametric Volterra Kernels with Gaussian Processes NeurIPS 2021 Learning partial correlation graphs and graphical models by covariance queries JMLR 2021 Learning the Parameters of Bayesian Networks from Uncertain Data AAAI 2021 Learning to Learn Dense Gaussian Processes for Few-Shot Learning NeurIPS 2021 Learning to Sample from Censored Markov Random Fields COLT 2021 Lenient Regret and Good-Action Identification in Gaussian Process Bandits ICML 2021 LEO: Learning Energy-based Models in Factor Graph Optimization CoRL 2021 LiBRe: A Practical Bayesian Approach to Adversarial Detection CVPR 2021 Lifted reasoning meets weighted model integration UAI 2021 Linearly Constrained Gaussian Processes with Boundary Conditions AISTATS 2021 Linear-Time Probabilistic Solution of Boundary Value Problems NeurIPS 2021 Locally Most Powerful Bayesian Test for Out-of-Distribution Detection using Deep Generative Models NeurIPS 2021 Location Trace Privacy Under Conditional Priors AISTATS 2021 Longitudinal Variational Autoencoder AISTATS 2021 Lower Bounds on Metropolized Sampling Methods for Well-Conditioned Distributions NeurIPS 2021 MAP Adaptation Characteristics in Forensic Long-Term Formant Analysis Interspeech 2021 Marginalised Gaussian Processes with Nested Sampling NeurIPS 2021 Markov equivalence of max-linear Bayesian networks UAI 2021 MARTA: Leveraging Human Rationales for Explainable Text Classification AAAI 2021