Probabilistic Modeling

Bayesian Manifold Learning: The Locally Linear Latent Variable Model (LL-LVM) NeurIPS 2015 Bayesian Masking: Sparse Bayesian Estimation with Weaker Shrinkage Bias ACML 2015 Bayesian Multiple Target Localization ICML 2015 Bounding errors of Expectation-Propagation NeurIPS 2015 Bounding the Cost of Search-Based Lifted Inference NeurIPS 2015 Class-prior Estimation for Learning from Positive and Unlabeled Data ACML 2015 COEVOLVE: A Joint Point Process Model for Information Diffusion and Network Co-evolution NeurIPS 2015 Convolutional spike-triggered covariance analysis for neural subunit models NeurIPS 2015 Cross-Domain Matching for Bag-of-Words Data via Kernel Embeddings of Latent Distributions NeurIPS 2015 Deep Poisson Factor Modeling NeurIPS 2015 Differentially Private Learning of Structured Discrete Distributions NeurIPS 2015 Efficient Learning of Continuous-Time Hidden Markov Models for Disease Progression NeurIPS 2015 Efficient Second-Order Gradient Boosting for Conditional Random Fields AISTATS 2015 Empirical Localization of Homogeneous Divergences on Discrete Sample Spaces NeurIPS 2015 Estimating Mixture Models via Mixtures of Polynomials NeurIPS 2015 Exactness of Approximate MAP Inference in Continuous MRFs NeurIPS 2015 Expectation Particle Belief Propagation NeurIPS 2015 Expectation Propagation for Rectified Linear Poisson Regression ACML 2015 Fast and Accurate Inference of Plackett–Luce Models NeurIPS 2015 Fast Classification Rates for High-dimensional Gaussian Generative Models NeurIPS 2015 Fast Lifted MAP Inference via Partitioning NeurIPS 2015 Fixed-point algorithms for learning determinantal point processes ICML 2015 Frank-Wolfe Bayesian Quadrature: Probabilistic Integration with Theoretical Guarantees NeurIPS 2015 Gaussian Process Random Fields NeurIPS 2015 GP Kernels for Cross-Spectrum Analysis NeurIPS 2015