Mathematics & Optimization

Greedy Algorithms for Structurally Constrained High Dimensional Problems NeurIPS 2011 Group Lasso Estimation of High-dimensional Covariance Matrices JMLR 2011 Hashing Algorithms for Large-Scale Learning NeurIPS 2011 Hierarchical Knowledge Gradient for Sequential Sampling JMLR 2011 High-dimensional Covariance Estimation Based On Gaussian Graphical Models JMLR 2011 High-Dimensional Graphical Model Selection: Tractable Graph Families and Necessary Conditions NeurIPS 2011 High-dimensional regression with noisy and missing data: Provable guarantees with non-convexity NeurIPS 2011 Higher-Order Correlation Clustering for Image Segmentation NeurIPS 2011 How biased are maximum entropy models? NeurIPS 2011 Improved Algorithms for Linear Stochastic Bandits NeurIPS 2011 Improved Moves for Truncated Convex Models JMLR 2011 Improved Regret Guarantees for Online Smooth Convex Optimization with Bandit Feedback AISTATS 2011 Inference in continuous-time change-point models NeurIPS 2011 Information, Divergence and Risk for Binary Experiments JMLR 2011 Information Theoretical Clustering via Semidefinite Programming AISTATS 2011 Internal Regret with Partial Monitoring: Calibration-Based Optimal Algorithms JMLR 2011 Learning Anchor Planes for Classification NeurIPS 2011 Learning a Robust Relevance Model for Search Using Kernel Methods JMLR 2011 Learning Auto-regressive Models from Sequence and Non-sequence Data NeurIPS 2011 Learning Eigenvectors for Free NeurIPS 2011 Learning High-Dimensional Markov Forest Distributions: Analysis of Error Rates JMLR 2011 Learning Higher-Order Graph Structure with Features by Structure Penalty NeurIPS 2011 Learning in Hilbert vs. Banach Spaces: A Measure Embedding Viewpoint NeurIPS 2011 Learning Latent Tree Graphical Models JMLR 2011 Learning low-rank output kernels ACML 2011