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Learning Theory

5312 directly classified papers

Papers per year

Papers

PAC-Bayesian Analysis of Contextual Bandits NIPS 2011
Stochastic convex optimization with bandit feedback NIPS 2011
Online Learning: Stochastic, Constrained, and Smoothed Adversaries NIPS 2011
Maximum Margin Multi-Label Structured Prediction NIPS 2011
Active Ranking using Pairwise Comparisons NIPS 2011
Sequence learning with hidden units in spiking neural networks NIPS 2011
The Fast Convergence of Boosting NIPS 2011
Shallow vs. Deep Sum-Product Networks NIPS 2011
Selecting the State-Representation in Reinforcement Learning NIPS 2011
Learning large-margin halfspaces with more malicious noise NIPS 2011
The Local Rademacher Complexity of Lp-Norm Multiple Kernel Learning NIPS 2011
On U-processes and clustering performance NIPS 2011
On Learning Discrete Graphical Models using Greedy Methods NIPS 2011
On the Universality of Online Mirror Descent NIPS 2011
Unifying Framework for Fast Learning Rate of Non-Sparse Multiple Kernel Learning NIPS 2011
Greedy Model Averaging NIPS 2011
Statistical Tests for Optimization Efficiency NIPS 2011
Multi-Bandit Best Arm Identification NIPS 2011
Noise Thresholds for Spectral Clustering NIPS 2011
From Bandits to Experts: On the Value of Side-Observations NIPS 2011
Efficient Learning of Generalized Linear and Single Index Models with Isotonic Regression NIPS 2011
Non-Asymptotic Analysis of Stochastic Approximation Algorithms for Machine Learning NIPS 2011
A Collaborative Mechanism for Crowdsourcing Prediction Problems NIPS 2011
A Reinforcement Learning Theory for Homeostatic Regulation NIPS 2011
Speedy Q-Learning NIPS 2011