Learning Theory

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?.