WhatToSee

Index of COLT 2008

• A Query Algorithm for Agnostically Learning DNF?
• Adapting to a Changing Environment: the Brownian Restless Bandits
• Adaptive Aggregation for Reinforcement Learning with Efficient Exploration: Deterministic Domains
• Adaptive Hausdorff Estimation of Density Level Sets
• Almost Tight Upper Bound for Finding Fourier Coefficients of Bounded Pseudo-Boolean Functions
• An Efficient Reduction of Ranking to Classification
• An Information Theoretic Framework for Multi-view Learning
• Combining Expert Advice Efficiently
• Competing in the Dark: An Efficient Algorithm for Bandit Linear Optimization
• Density estimation in linear time
• Dimension and Margin Bounds for Reflection-invariant Kernels
• Does Unlabeled Data Provably Help? Worst-case Analysis of the Sample Complexity of Semi-Supervised Learning
• Extracting Certainty from Uncertainty: Regret Bounded by Variation in Costs
• Finding Metric Structure in Information Theoretic Clustering
• Geometric & Topological Representations of Maximum Classes with Applications to Sample Compression
• High-Probability Regret Bounds for Bandit Online Linear Optimization
• How Local Should a Learning Method Be?
• Improved Guarantees for Learning via Similarity Functions
• Injective Hilbert Space Embeddings of Probability Measures
• Learning Acyclic Probabilistic Circuits Using Test Paths
• Learning Mixtures of Product Distributions using Correlations and Independence
• Learning Random Monotone DNF Under the Uniform Distribution
• Learning Rotations
• Learning coordinate gradients with multi-task kernels
• Learning from Collective Behavior
• Learning in the Limit with Adversarial Disturbances
• Linear Algorithms for Online Multitask Classification
• Minimizing Wide Range Regret with Time Selection Functions
• Model Selection and Stability in k -means Clustering
• More Efficient Internal-Regret-Minimizing Algorithms
• On The Power of Membership Queries in Agnostic Learning
• On the Equivalence of Weak Learnability and Linear Separability: New Relaxations and Efficient Boosting Algorithms
• On the Margin Explanation of Bo osting Algorithms
• On-line sequential bin packing
• Online Learning of Approximate Maximum p-Norm Margin Classifiers with Bias
• Optimal Strategies and Minimax Lower Bounds for Online Convex Games
• Optimal Strategies from Random Walks
• Polynomial regression under arbitrary product distributions
• Regret Bounds for Sleeping Experts and Bandits
• Relating clustering stability to properties of cluster boundaries
• Sparse Recovery in Large Ensembles of Kernel Machines
• Stochastic Linear Optimization under Bandit Feedback
• Teaching Dimensions Based on Cooperative Learning
• The Learning Power of Evolution
• The True Sample Complexity of Active Learning
• Time Varying Undirected Graphs