Learning Theory

Learning with Consistency between Inductive Functions and Kernels NeurIPS 2008 Linear Classification and Selective Sampling Under Low Noise Conditions NeurIPS 2008 Look Ma, No Hands: Analyzing the Monotonic Feature Abstraction for Text Classification NeurIPS 2008 Mind the Duality Gap: Logarithmic regret algorithms for online optimization NeurIPS 2008 Nearly Uniform Validation Improves Compression-Based Error Bounds JMLR 2008 Near-optimal Regret Bounds for Reinforcement Learning NeurIPS 2008 On the asymptotic equivalence between differential Hebbian and temporal difference learning using a local third factor NeurIPS 2008 On the Complexity of Linear Prediction: Risk Bounds, Margin Bounds, and Regularization NeurIPS 2008 On the Generalization Ability of Online Strongly Convex Programming Algorithms NeurIPS 2008 Phase transitions for high-dimensional joint support recovery NeurIPS 2008 Posterior Consistency of the Silverman g-prior in Bayesian Model Choice NeurIPS 2008 Rademacher Complexity Bounds for Non-I.I.D. Processes NeurIPS 2008 Ranking Categorical Features Using Generalization Properties JMLR 2008 Relative Performance Guarantees for Approximate Inference in Latent Dirichlet Allocation NeurIPS 2008 Resolution Limits of Sparse Coding in High Dimensions NeurIPS 2008 Risk Bounds for Randomized Sample Compressed Classifiers NeurIPS 2008 Sparsity of SVMs that use the epsilon-insensitive loss NeurIPS 2008 Theory of matching pursuit NeurIPS 2008 Unlabeled data: Now it helps, now it doesn't NeurIPS 2008 Using Markov Blankets for Causal Structure Learning JMLR 2008 Weighted Sums of Random Kitchen Sinks: Replacing minimization with randomization in learning NeurIPS 2008 AdaBoost is Consistent JMLR 2007 Adaptive Online Gradient Descent NeurIPS 2007 A general agnostic active learning algorithm NeurIPS 2007 A learning framework for nearest neighbor search NeurIPS 2007