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Learning Theory

5312 directly classified papers

Papers per year

Papers

Slow Learners are Fast NIPS 2009
Functional network reorganization in motor cortex can be explained by reward-modulated Hebbian learning NIPS 2009
Human Rademacher Complexity NIPS 2009
Asymptotically Optimal Regularization in Smooth Parametric Models NIPS 2009
Fast, smooth and adaptive regression in metric spaces NIPS 2009
Information-theoretic lower bounds on the oracle complexity of convex optimization NIPS 2009
Which graphical models are difficult to learn? NIPS 2009
Potential-Based Agnostic Boosting NIPS 2009
Code-specific policy gradient rules for spiking neurons NIPS 2009
Generalization Errors and Learning Curves for Regression with Multi-task Gaussian Processes NIPS 2009
Fast Learning from Non-i.i.d. Observations NIPS 2009
Data-driven calibration of linear estimators with minimal penalties NIPS 2009
Learning Permutations with Exponential Weights JMLR 2009
Consistency and Localizability JMLR 2009
Learning Acyclic Probabilistic Circuits Using Test Paths JMLR 2009
On Uniform Deviations of General Empirical Risks with Unboundedness, Dependence, and High Dimensionality JMLR 2009
Controlling the False Discovery Rate of the Association/Causality Structure Learned with the PC Algorithm JMLR 2009
On the Consistency of Feature Selection using Greedy Least Squares Regression JMLR 2009
From PAC-Bayes Bounds to KL Regularization NIPS 2009
STDP enables spiking neurons to detect hidden causes of their inputs NIPS 2009
Compressed Least-Squares Regression NIPS 2009
Learning Halfspaces with Malicious Noise JMLR 2009
Measuring model complexity with the prior predictive NIPS 2009
Prediction With Expert Advice For The Brier Game JMLR 2009
Markov Properties for Linear Causal Models with Correlated Errors JMLR 2009