Invited Presentations.- Property Testing: A Learning Theory Perspective.- Spectral Algorithms for Learning and Clustering.- Unsupervised, Semisupervised and Active Learning I.- Minimax Bounds for Active Learning.- Stability of k-Means Clustering.- Margin Based Active Learning.- Unsupervised, Semisupervised and Active Learning II.- Learning Large-Alphabet and Analog Circuits with Value Injection Queries.- Teaching Dimension and the Complexity of Active Learning.- Multi-view Regression Via Canonical Correlation Analysis.- Statistical Learning Theory.- Aggregation by Exponential Weighting and Sharp Oracle Inequalities.- Occam's Hammer.- Resampling-Based Confidence Regions and Multiple Tests for a Correlated Random Vector.- Suboptimality of Penalized Empirical Risk Minimization in Classification.- Transductive Rademacher Complexity and Its Applications.- Inductive Inference.- U-Shaped, Iterative, and Iterative-with-Counter Learning.- Mind Change Optimal Learning of Bayes Net Structure.- Learning Correction Grammars.- Mitotic Classes.- Online and Reinforcement Learning I.- Regret to the Best vs. Regret to the Average.- Strategies for Prediction Under Imperfect Monitoring.- Bounded Parameter Markov Decision Processes with Average Reward Criterion.- Online and Reinforcement Learning II.- On-Line Estimation with the Multivariate Gaussian Distribution.- Generalised Entropy and Asymptotic Complexities of Languages.- Q-Learning with Linear Function Approximation.- Regularized Learning, Kernel Methods, SVM.- How Good Is a Kernel When Used as a Similarity Measure?.- Gaps in Support Vector Optimization.- Learning Languages with Rational Kernels.- Generalized SMO-Style Decomposition Algorithms.- Learning Algorithms and Limitations on Learning.- Learning Nested Halfspaces and UphillDecision Trees.- An Efficient Re-scaled Perceptron Algorithm for Conic Systems.- A Lower Bound for Agnostically Learning Disjunctions.- Sketching Information Divergences.- Competing with Stationary Prediction Strategies.- Online and Reinforcement Learning III.- Improved Rates for the Stochastic Continuum-Armed Bandit Problem.- Learning Permutations with Exponential Weights.- Online and Reinforcement Learning IV.- Multitask Learning with Expert Advice.- Online Learning with Prior Knowledge.- Dimensionality Reduction.- Nonlinear Estimators and Tail Bounds for Dimension Reduction in l 1 Using Cauchy Random Projections.- Sparse Density Estimation with ?1 Penalties.- ?1 Regularization in Infinite Dimensional Feature Spaces.- Prediction by Categorical Features: Generalization Properties and Application to Feature Ranking.- Other Approaches.- Observational Learning in Random Networks.- The Loss Rank Principle for Model Selection.- Robust Reductions from Ranking to Classification.- Open Problems.- Rademacher Margin Complexity.- Open Problems in Efficient Semi-supervised PAC Learning.- Resource-Bounded Information Gathering for Correlation Clustering.- Are There Local Maxima in the Infinite-Sample Likelihood of Gaussian Mixture Estimation?.- When Is There a Free Matrix Lunch?.
Inhaltsverzeichnis
Invited Presentations. - Property Testing: A Learning Theory Perspective. - Spectral Algorithms for Learning and Clustering. - Unsupervised, Semisupervised and Active Learning I. - Minimax Bounds for Active Learning. - Stability of k-Means Clustering. - Margin Based Active Learning. - Unsupervised, Semisupervised and Active Learning II. - Learning Large-Alphabet and Analog Circuits with Value Injection Queries. - Teaching Dimension and the Complexity of Active Learning. - Multi-view Regression Via Canonical Correlation Analysis. - Statistical Learning Theory. - Aggregation by Exponential Weighting and Sharp Oracle Inequalities. - Occam s Hammer. - Resampling-Based Confidence Regions and Multiple Tests for a Correlated Random Vector. - Suboptimality of Penalized Empirical Risk Minimization in Classification. - Transductive Rademacher Complexity and Its Applications. - Inductive Inference. - U-Shaped, Iterative, and Iterative-with-Counter Learning. - Mind Change Optimal Learning of Bayes Net Structure. - Learning Correction Grammars. - Mitotic Classes. - Online and Reinforcement Learning I. - Regret to the Best vs. Regret to the Average. - Strategies for Prediction Under Imperfect Monitoring. - Bounded Parameter Markov Decision Processes with Average Reward Criterion. - Online and Reinforcement Learning II. - On-Line Estimation with the Multivariate Gaussian Distribution. - Generalised Entropy and Asymptotic Complexities of Languages. - Q-Learning with Linear Function Approximation. - Regularized Learning, Kernel Methods, SVM. - How Good Is a Kernel When Used as a Similarity Measure? . - Gaps in Support Vector Optimization. - Learning Languages with Rational Kernels. - Generalized SMO-Style Decomposition Algorithms. - Learning Algorithms and Limitations on Learning. - Learning Nested Halfspaces and UphillDecision Trees. - An Efficient Re-scaled Perceptron Algorithm for Conic Systems. - A Lower Bound for Agnostically Learning Disjunctions. - Sketching Information Divergences. - Competing with Stationary Prediction Strategies. - Online and Reinforcement Learning III. - Improved Rates for the Stochastic Continuum-Armed Bandit Problem. - Learning Permutations with Exponential Weights. - Online and Reinforcement Learning IV. - Multitask Learning with Expert Advice. - Online Learning with Prior Knowledge. - Dimensionality Reduction. - Nonlinear Estimators and Tail Bounds for Dimension Reduction in l 1 Using Cauchy Random Projections. - Sparse Density Estimation with ? 1 Penalties. - ? 1 Regularization in Infinite Dimensional Feature Spaces. - Prediction by Categorical Features: Generalization Properties and Application to Feature Ranking. - Other Approaches. - Observational Learning in Random Networks. - The Loss Rank Principle for Model Selection. - Robust Reductions from Ranking to Classification. - Open Problems. - Rademacher Margin Complexity. - Open Problems in Efficient Semi-supervised PAC Learning. - Resource-Bounded Information Gathering for Correlation Clustering. - Are There Local Maxima in the Infinite-Sample Likelihood of Gaussian Mixture Estimation? . - When Is There a Free Matrix Lunch? .
