Mathematics & Optimization

Group Sparse Coding with a Laplacian Scale Mixture Prior NeurIPS 2010 Guaranteed Rank Minimization via Singular Value Projection NeurIPS 2010 Guarantees for Approximate Incremental SVMs AISTATS 2010 Hierarchical Convex NMF for Clustering Massive Data ACML 2010 High Dimensional Inverse Covariance Matrix Estimation via Linear Programming JMLR 2010 High-dimensional Variable Selection with Sparse Random Projections: Measurement Sparsity and Statistical Efficiency JMLR 2010 Hilbert Space Embeddings and Metrics on Probability Measures JMLR 2010 Identifying Dendritic Processing NeurIPS 2010 Identifying graph-structured activation patterns in networks NeurIPS 2010 Importance Sampling for Continuous Time Bayesian Networks JMLR 2010 Improving the Asymptotic Performance of Markov Chain Monte-Carlo by Inserting Vortices NeurIPS 2010 Incremental Sampling-based Algorithms for Optimal Motion Planning RSS 2010 Incremental Sparsification for Real-time Online Model Learning AISTATS 2010 Individualized ROI Optimization via Maximization of Group-wise Consistency of Structural and Functional Profiles NeurIPS 2010 Inference of Sparse Networks with Unobserved Variables. Application to Gene Regulatory Networks AISTATS 2010 Inference with Multivariate Heavy-Tails in Linear Models NeurIPS 2010 Iterative Scaling and Coordinate Descent Methods for Maximum Entropy Models JMLR 2010 Kernel Partial Least Squares is Universally Consistent AISTATS 2010 Kronecker Graphs: An Approach to Modeling Networks JMLR 2010 Large-Scale Matrix Factorization with Missing Data under Additional Constraints NeurIPS 2010 Learning Bayesian Network Structure using LP Relaxations AISTATS 2010 Learning Causal Structure from Overlapping Variable Sets AISTATS 2010 Learning Gradients: Predictive Models that Infer Geometry and Statistical Dependence JMLR 2010 Learning Multiple Tasks with a Sparse Matrix-Normal Penalty NeurIPS 2010 Learning Networks of Stochastic Differential Equations NeurIPS 2010