Bayesian & Probabilistic

Matrix-Variate Dirichlet Process Mixture Models AISTATS 2010 Maximum-likelihood learning of cumulative distribution functions on graphs AISTATS 2010 Mixture of time-warped trajectory models for movement decoding NeurIPS 2010 Modeling annotator expertise: Learning when everybody knows a bit of something AISTATS 2010 More data means less inference: A pseudo-max approach to structured learning NeurIPS 2010 Movement extraction by detecting dynamics switches and repetitions NeurIPS 2010 Multi-Task Learning using Generalized t Process AISTATS 2010 Neural conditional random fields AISTATS 2010 Noise-contrastive estimation: A new estimation principle for unnormalized statistical models AISTATS 2010 Nonparametric Bayesian Matrix Factorization by Power-EP AISTATS 2010 Nonparametric Bayesian Policy Priors for Reinforcement Learning NeurIPS 2010 Nonparametric Density Estimation for Stochastic Optimization with an Observable State Variable NeurIPS 2010 Nonparametric prior for adaptive sparsity AISTATS 2010 On Combining Graph-based Variance Reduction schemes AISTATS 2010 On Herding and the Perceptron Cycling Theorem NeurIPS 2010 Online Learning for Latent Dirichlet Allocation NeurIPS 2010 On the Convergence Properties of Contrastive Divergence AISTATS 2010 PAC-Bayesian Model Selection for Reinforcement Learning NeurIPS 2010 Parallelizable Sampling of Markov Random Fields AISTATS 2010 Polynomial-Time Exact Inference in NP-Hard Binary MRFs via Reweighted Perfect Matching AISTATS 2010 Predictive Subspace Learning for Multi-view Data: a Large Margin Approach NeurIPS 2010 Probabilistic Belief Revision with Structural Constraints NeurIPS 2010 Probabilistic Deterministic Infinite Automata NeurIPS 2010 Probabilistic Inference and Differential Privacy NeurIPS 2010 Probabilistic latent variable models for distinguishing between cause and effect NeurIPS 2010