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Learning Theory

5,334 papers

Papers per year

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466
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Papers

Risk Bounds for Levy Processes in the PAC-Learning Framework AISTATS 2010
Model Selection: Beyond the Bayesian/Frequentist Divide JMLR 2010
Stability Bounds for Stationary φ-mixing and β-mixing Processes JMLR 2010
On Learning with Integral Operators JMLR 2010
Stochastic Complexity and Generalization Error of a Restricted Boltzmann Machine in Bayesian Estimation JMLR 2010
Near-optimal Regret Bounds for Reinforcement Learning JMLR 2010
Chromatic PAC-Bayes Bounds for Non-IID Data: Applications to Ranking and Stationary β-Mixing Processes JMLR 2010
On Over-fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation JMLR 2010
Sparse Semi-supervised Learning Using Conjugate Functions JMLR 2010
Rademacher Complexities and Bounding the Excess Risk in Active Learning JMLR 2010
Hubs in Space: Popular Nearest Neighbors in High-Dimensional Data JMLR 2010
Learnability, Stability and Uniform Convergence JMLR 2010
Using Contextual Representations to Efficiently Learn Context-Free Languages JMLR 2010
Regret Bounds and Minimax Policies under Partial Monitoring JMLR 2010
Asymptotic Equivalence of Bayes Cross Validation and Widely Applicable Information Criterion in Singular Learning Theory JMLR 2010
Closing the Learning-Planning Loop with Predictive State Representations RSS 2010
Compressed Least-Squares Regression NIPS 2009
Code-specific policy gradient rules for spiking neurons NIPS 2009
Potential-Based Agnostic Boosting NIPS 2009
Which graphical models are difficult to learn? NIPS 2009
Information-theoretic lower bounds on the oracle complexity of convex optimization NIPS 2009
From PAC-Bayes Bounds to KL Regularization NIPS 2009
Fast, smooth and adaptive regression in metric spaces NIPS 2009
Asymptotically Optimal Regularization in Smooth Parametric Models NIPS 2009
Statistical Analysis of Semi-Supervised Learning: The Limit of Infinite Unlabelled Data NIPS 2009