← Core Methods
Machine Learning › Core Methods ›

Probabilistic Modeling

363 directly classified papers

Papers per year

Papers

A provable SVD-based algorithm for learning topics in dominant admixture corpus NIPS 2014
Compressive Sensing of Signals from a GMM with Sparse Precision Matrices NIPS 2014
Covariance shrinkage for autocorrelated data NIPS 2014
Uniqueness of Tensor Decompositions with Applications to Polynomial Identifiability COLT 2014
Distance-Based Network Recovery under Feature Correlation NIPS 2014
Bayes-Optimal Scorers for Bipartite Ranking COLT 2014
Open Problem: Finding Good Cascade Sampling Processes for the Network Inference Problem COLT 2014
Expectation-Maximization for Learning Determinantal Point Processes NIPS 2014
Spectral Methods meet EM: A Provably Optimal Algorithm for Crowdsourcing NIPS 2014
Diverse Sequential Subset Selection for Supervised Video Summarization NIPS 2014
Learning Mixtures of Discrete Product Distributions using Spectral Decompositions COLT 2014
Nonparametric Estimation of Multi-View Latent Variable Models ICML 2014
Robust Inverse Covariance Estimation under Noisy Measurements ICML 2014
Hardness of parameter estimation in graphical models NIPS 2014
Flexible sampling of discrete data correlations without the marginal distributions NIPS 2013
Bethe Bounds and Approximating the Global Optimum AISTATS 2013
Squared-loss Mutual Information Regularization: A Novel Information-theoretic Approach to Semi-supervised Learning ICML 2013
Hierarchical Tensor Decomposition of Latent Tree Graphical Models ICML 2013
Modeling Information Propagation with Survival Theory ICML 2013
Higher Order Priors for Joint Intrinsic Image, Objects, and Attributes Estimation NIPS 2013
Similarity Component Analysis NIPS 2013
Embed and Project: Discrete Sampling with Universal Hashing NIPS 2013
Geometric optimisation on positive definite matrices for elliptically contoured distributions NIPS 2013
Contrastive Learning Using Spectral Methods NIPS 2013
Bayesian Hierarchical Community Discovery NIPS 2013