Graphical Models

End to end learning and optimization on graphs NeurIPS 2019 Exact inference in structured prediction NeurIPS 2019 Exact Sampling of Directed Acyclic Graphs from Modular Distributions UAI 2019 Expressive power of tensor-network factorizations for probabilistic modeling NeurIPS 2019 Fast Relational Probabilistic Inference and Learning: Approximate Counting via Hypergraphs AAAI 2019 Fast structure learning with modular regularization NeurIPS 2019 Finding Minimal d-separators in Linear Time and Applications UAI 2019 Forbidden Nodes Aware Community Search AAAI 2019 Forward-Backward Selection with Early Dropping JMLR 2019 Graphical Lasso and Thresholding: Equivalence and Closed-form Solutions JMLR 2019 Graph Structured Prediction Energy Networks NeurIPS 2019 Guaranteed Scalable Learning of Latent Tree Models UAI 2019 High-Dimensional Poisson Structural Equation Model Learning via ℓ_1-Regularized Regression JMLR 2019 Identifying Android Malware Using Network-Based Approaches AAAI 2019 Incorporating Network Embedding into Markov Random Field for Better Community Detection AAAI 2019 Informative Object Annotations: Tell Me Something I Don't Know CVPR 2019 Layer-Wise Learning Strategy for Nonparametric Tensor Product Smoothing Spline Regression and Graphical Models JMLR 2019 Learning Diverse Bayesian Networks AAAI 2019 Learning Non-Uniform Hypergraph for Multi-Object Tracking AAAI 2019 Learning the Structure of a Nonstationary Vector Autoregression AISTATS 2019 Lifted Hinge-Loss Markov Random Fields AAAI 2019 Marginal Inference in Continuous Markov Random Fields Using Mixtures AAAI 2019 Markov Random Fields for Collaborative Filtering NeurIPS 2019 One-Shot Marginal MAP Inference in Markov Random Fields UAI 2019 On Lifted Inference Using Neural Embeddings AAAI 2019