Probabilistic Modeling

Probabilistic Partial Canonical Correlation Analysis ICML 2014 Probabilistic Solutions to Differential Equations and their Application to Riemannian Statistics AISTATS 2014 Putting MRFs on a Tensor Train ICML 2014 Recursive Inversion Models for Permutations NeurIPS 2014 Restricted Boltzmann machines modeling human choice NeurIPS 2014 Revisiting Bayesian Blind Deconvolution JMLR 2014 Robust learning of inhomogeneous PMMs AISTATS 2014 Robust Principal Component Analysis with Complex Noise ICML 2014 Sample Efficient Reinforcement Learning with Gaussian Processes ICML 2014 Scalable Inference for Neuronal Connectivity from Calcium Imaging NeurIPS 2014 Scaling-up Importance Sampling for Markov Logic Networks NeurIPS 2014 Sequential Monte Carlo for Graphical Models NeurIPS 2014 Signal Aggregate Constraints in Additive Factorial HMMs, with Application to Energy Disaggregation NeurIPS 2014 Simple MAP Inference via Low-Rank Relaxations NeurIPS 2014 SMERED: A Bayesian Approach to Graphical Record Linkage and De-duplication AISTATS 2014 Sparse meta-Gaussian information bottleneck ICML 2014 Spatio-temporal Representations of Uncertainty in Spiking Neural Networks NeurIPS 2014 Spectral Learning of Mixture of Hidden Markov Models NeurIPS 2014 Spectral Methods for Indian Buffet Process Inference NeurIPS 2014 Stochastic variational inference for hidden Markov models NeurIPS 2014 Structured Recurrent Temporal Restricted Boltzmann Machines ICML 2014 Structure learning of antiferromagnetic Ising models NeurIPS 2014 Student-t Processes as Alternatives to Gaussian Processes AISTATS 2014 The Bayesian Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classification NeurIPS 2014 The Infinite Mixture of Infinite Gaussian Mixtures NeurIPS 2014