Bayesian & Probabilistic

Solving inverse problem of Markov chain with partial observations NeurIPS 2013 Sparse Adaptive Dirichlet-Multinomial-like Processes COLT 2013 Sparse Gaussian Conditional Random Fields: Algorithms, Theory, and Application to Energy Forecasting ICML 2013 Sparse Inverse Covariance Estimation with Calibration NeurIPS 2013 Spectral Learning of Hidden Markov Models from Dynamic and Static Data ICML 2013 Spectral methods for neural characterization using generalized quadratic models NeurIPS 2013 Spike train entropy-rate estimation using hierarchical Dirichlet process priors NeurIPS 2013 Squared-loss Mutual Information Regularization: A Novel Information-theoretic Approach to Semi-supervised Learning ICML 2013 Stochastic Gradient Riemannian Langevin Dynamics on the Probability Simplex NeurIPS 2013 Streaming Variational Bayes NeurIPS 2013 Structural Expectation Propagation (SEP): Bayesian structure learning for networks with latent variables AISTATS 2013 Structure Learning of Mixed Graphical Models AISTATS 2013 Subproblem-Tree Calibration: A Unified Approach to Max-Product Message Passing ICML 2013 Summary Statistics for Partitionings and Feature Allocations NeurIPS 2013 The Bigraphical Lasso ICML 2013 Thompson Sampling for 1-Dimensional Exponential Family Bandits NeurIPS 2013 Tracking People and Their Objects CVPR 2013 Tracking Time-varying Graphical Structure NeurIPS 2013 Universal models for binary spike patterns using centered Dirichlet processes NeurIPS 2013 Unsupervised Structure Learning of Stochastic And-Or Grammars NeurIPS 2013 Using multiple samples to learn mixture models NeurIPS 2013 Variance Reduction for Stochastic Gradient Optimization NeurIPS 2013 Variational Inference for Mahalanobis Distance Metrics in Gaussian Process Regression NeurIPS 2013 Variational Planning for Graph-based MDPs NeurIPS 2013 Visual Concept Learning: Combining Machine Vision and Bayesian Generalization on Concept Hierarchies NeurIPS 2013