← Optimization & Theory
Machine Learning › Optimization & Theory ›

Learning Theory

5312 directly classified papers

Papers per year

Papers

Estimating Diffusion Networks: Recovery Conditions, Sample Complexity and Soft-thresholding Algorithm JMLR 2016
Multiscale Dictionary Learning: Non-Asymptotic Bounds and Robustness JMLR 2016
A ranking approach to global optimization ICML 2016
The Constrained Dantzig Selector with Enhanced Consistency JMLR 2016
Fast learning rates with heavy-tailed losses NIPS 2016
The Teaching Dimension of Linear Learners JMLR 2016
Group Equivariant Convolutional Networks ICML 2016
Tight Complexity Bounds for Optimizing Composite Objectives NIPS 2016
On the Recursive Teaching Dimension of VC Classes NIPS 2016
The Power of Optimization from Samples NIPS 2016
Learning Theory for Distribution Regression JMLR 2016
A General Framework for Consistency of Principal Component Analysis JMLR 2016
L1-Regularized Least Squares for Support Recovery of High Dimensional Single Index Models with Gaussian Designs JMLR 2016
Iterative Regularization for Learning with Convex Loss Functions JMLR 2016
Learning Bound for Parameter Transfer Learning NIPS 2016
Generalization and Exploration via Randomized Value Functions ICML 2016
Preference-based Teaching COLT 2016
Gains and Losses are Fundamentally Different in Regret Minimization: The Sparse Case JMLR 2016
Stability and Generalization in Structured Prediction JMLR 2016
Optimal Learning Rates for Localized SVMs JMLR 2016
Learning with Differential Privacy: Stability, Learnability and the Sufficiency and Necessity of ERM Principle JMLR 2016
Consistency of Cheeger and Ratio Graph Cuts JMLR 2016
A Note on the Sample Complexity of the Er-SpUD Algorithm by Spielman, Wang and Wright for Exact Recovery of Sparsely Used Dictionaries JMLR 2016
The Teaching Dimension of Linear Learners ICML 2016
On the Consistency of Feature Selection With Lasso for Non-linear Targets ICML 2016