Probabilistic Modeling

Learning Bayesian Networks from Ordinal Data JMLR 2021 Learning Deep Latent Variable Models by Short-Run MCMC Inference With Optimal Transport Correction CVPR 2021 Learning Icosahedral Spherical Probability Map Based on Bingham Mixture Model for Vanishing Point Estimation ICCV 2021 Learning Logic Rules for Document-Level Relation Extraction EMNLP 2021 Learning Partially Known Stochastic Dynamics with Empirical PAC Bayes AISTATS 2021 Learning Probabilistic Ordinal Embeddings for Uncertainty-Aware Regression CVPR 2021 Learning Shared Subgraphs in Ising Model Pairs AISTATS 2021 Learning to Sample from Censored Markov Random Fields COLT 2021 Lifted reasoning meets weighted model integration UAI 2021 Light RUMs ICML 2021 Locally Most Powerful Bayesian Test for Out-of-Distribution Detection using Deep Generative Models NeurIPS 2021 Longitudinal Deep Kernel Gaussian Process Regression AAAI 2021 Minimax Estimation of Laplacian Constrained Precision Matrices AISTATS 2021 Modeling Risky Choices in Unknown Environments ACML 2021 Moment Multicalibration for Uncertainty Estimation COLT 2021 Necessary and sufficient graphical conditions for optimal adjustment sets in causal graphical models with hidden variables NeurIPS 2021 Neural Temporal Point Processes: A Review IJCAI 2021 Non-approximate Inference for Collective Graphical Models on Path Graphs via Discrete Difference of Convex Algorithm NeurIPS 2021 Nonparametric Hamiltonian Monte Carlo ICML 2021 On Energy-Based Models with Overparametrized Shallow Neural Networks ICML 2021 On Estimation in Latent Variable Models ICML 2021 On the difficulty of unbiased alpha divergence minimization ICML 2021 On the Stochastic Stability of Deep Markov Models NeurIPS 2021 Parametric Distributions to Model Numerical Emotion Labels Interspeech 2021 Pareto GAN: Extending the Representational Power of GANs to Heavy-Tailed Distributions ICML 2021