conftrace_
← Optimization & Theory
Machine Learning › Optimization & Theory ›

Statistical Learning

4,117 papers

Papers per year

9
62
100
382
192
'05
'10
'15
'20
'25

Papers

One-Pass AUC Optimization ICML 2013
Stability and Hypothesis Transfer Learning ICML 2013
Breaking the Small Cluster Barrier of Graph Clustering ICML 2013
The lasso, persistence, and cross-validation ICML 2013
Spectral Experts for Estimating Mixtures of Linear Regressions ICML 2013
Distribution to Distribution Regression ICML 2013
Optimizing the F-Measure in Multi-Label Classification: Plug-in Rule Approach versus Structured Loss Minimization ICML 2013
Multiple-source cross-validation ICML 2013
Cost-sensitive Multiclass Classification Risk Bounds ICML 2013
Self-Organized Neural Learning of Statistical Inference from High-Dimensional Data IJCAI 2013
A Widely Applicable Bayesian Information Criterion JMLR 2013
Asymptotic Results on Adaptive False Discovery Rate Controlling Procedures Based on Kernel Estimators JMLR 2013
A Risk Comparison of Ordinary Least Squares vs Ridge Regression JMLR 2013
Sub-Local Constraint-Based Learning of Bayesian Networks Using A Joint Dependence Criterion JMLR 2013
Nonparametric Sparsity and Regularization JMLR 2013
Variable Selection in High-Dimension with Random Designs and Orthogonal Matching Pursuit JMLR 2013
Alleviating Naive Bayes Attribute Independence Assumption by Attribute Weighting JMLR 2013
Distribution-Dependent Sample Complexity of Large Margin Learning JMLR 2013
On the Mutual Nearest Neighbors Estimate in Regression JMLR 2013
A Plug-in Approach to Neyman-Pearson Classification JMLR 2013
Communication-Efficient Algorithms for Statistical Optimization JMLR 2013
Consistent Selection of Tuning Parameters via Variable Selection Stability JMLR 2013
Comment on "Robustness and Regularization of Support Vector Machines" by H. Xu et al. (Journal of Machine Learning Research, vol. 10, pp. 1485-1510, 2009) JMLR 2013
Optimal Neural Tuning Curves for Arbitrary Stimulus Distributions: Discrimax, Infomax and Minimum $L_p$ Loss NIPS 2012
Privacy Aware Learning NIPS 2012