Bayesian & Probabilistic

Learning decomposable models by coarsening PGM 2020 Learning Discrete Energy-based Models via Auxiliary-variable Local Exploration NeurIPS 2020 Learning from Binary Multiway Data: Probabilistic Tensor Decomposition and its Statistical Optimality JMLR 2020 Learning from Positive and Unlabeled Data without Explicit Estimation of Class Prior AAAI 2020 Learning General Latent-Variable Graphical Models with Predictive Belief Propagation AAAI 2020 Learning identifiable and interpretable latent models of high-dimensional neural activity using pi-VAE NeurIPS 2020 Learning Informative Representations of Biomedical Relations with Latent Variable Models EMNLP 2020 Learning in the Wild with Incremental Skeptical Gaussian Processes IJCAI 2020 Learning Joint Nonlinear Effects from Single-variable Interventions in the Presence of Hidden Confounders UAI 2020 Learning Model with Error -- Exposing the Hidden Model of BAYHENN IJCAI 2020 Learning of Sub-optimal Gait Controllers for Magnetic Walking Soft Millirobots RSS 2020 Learning Probabilistic Sentence Representations from Paraphrases ACL 2020 Learning Restricted Boltzmann Machines with Sparse Latent Variables NeurIPS 2020 Learning Robot Skills with Temporal Variational Inference ICML 2020 Learning supported Model Predictive Control for Tracking of Periodic References L4DC 2020 Learning to Learn Kernels with Variational Random Features ICML 2020 Learning to Reason: Leveraging Neural Networks for Approximate DNF Counting AAAI 2020 Learning under Model Misspecification: Applications to Variational and Ensemble methods NeurIPS 2020 Learning VAE-LDA Models with Rounded Reparameterization Trick EMNLP 2020 Learning Weighted Model Integration Distributions AAAI 2020 Learning Weighted Submanifolds With Variational Autoencoders and Riemannian Variational Autoencoders CVPR 2020 Lifted Hybrid Variational Inference IJCAI 2020 Lifted Query Answering in Gaussian Bayesian Networks PGM 2020 LMLFM: Longitudinal Multi-Level Factorization Machine AAAI 2020 Localized active learning of Gaussian process state space models L4DC 2020