Probabilistic Modeling

High-dimensional neural spike train analysis with generalized count linear dynamical systems NeurIPS 2015 Indoor Scene Structure Analysis for Single Image Depth Estimation CVPR 2015 Inference in a Partially Observed Queuing Model with Applications in Ecology ICML 2015 Inverse Reinforcement Learning with Locally Consistent Reward Functions NeurIPS 2015 Joint Probabilistic Data Association Revisited ICCV 2015 Kullback-Leibler Proximal Variational Inference NeurIPS 2015 Large-Scale Bayesian Multi-Label Learning via Topic-Based Label Embeddings NeurIPS 2015 Large-scale probabilistic predictors with and without guarantees of validity NeurIPS 2015 Latent Bayesian melding for integrating individual and population models NeurIPS 2015 Latent feature regression for multivariate count data AISTATS 2015 Latent Trees for Estimating Intensity of Facial Action Units CVPR 2015 Learning spatiotemporal trajectories from manifold-valued longitudinal data NeurIPS 2015 Learning structured densities via infinite dimensional exponential families NeurIPS 2015 Lifted Symmetry Detection and Breaking for MAP Inference NeurIPS 2015 Linear Response Methods for Accurate Covariance Estimates from Mean Field Variational Bayes NeurIPS 2015 Low-Rank Spectral Learning with Weighted Loss Functions AISTATS 2015 Matrix Manifold Optimization for Gaussian Mixtures NeurIPS 2015 Maximum Likelihood Learning With Arbitrary Treewidth via Fast-Mixing Parameter Sets NeurIPS 2015 MCMC Learning COLT 2015 Measuring Sample Quality with Stein's Method NeurIPS 2015 Message Passing for Collective Graphical Models ICML 2015 Model Selection for Topic Models via Spectral Decomposition AISTATS 2015 Nonparametric Bayesian Factor Analysis for Dynamic Count Matrices AISTATS 2015 On some provably correct cases of variational inference for topic models NeurIPS 2015 On the Accuracy of Self-Normalized Log-Linear Models NeurIPS 2015