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Generalization Bounds

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TitleStatusHype
Perceptron-like Algorithms and Generalization Bounds for Learning to Rank—0
Maximum Margin Multiclass Nearest Neighbors—0
Generalization Bounds for Representative Domain Adaptation—0
Sample Complexity of Dictionary Learning and other Matrix Factorizations—0
Multi-Task Classification Hypothesis Space with Improved Generalization Bounds—0
Learning Multiple Models via Regularized Weighting—0
Relative Deviation Learning Bounds and Generalization with Unbounded Loss Functions—0
Bennett-type Generalization Bounds: Large-deviation Case and Faster Rate of Convergence—0
Supervised Metric Learning with Generalization Guarantees—0
A PAC-Bayesian Tutorial with A Dropout Bound—0
Guaranteed Classification via Regularized Similarity Learning—0
Tight Lower Bound on the Probability of a Binomial Exceeding its Expectation—0
Boosting with the Logistic Loss is Consistent—0
On the Generalization Ability of Online Learning Algorithms for Pairwise Loss Functions—0
Generalization Bounds for Domain Adaptation—0
Graph-based Generalization Bounds for Learning Binary Relations—0
Adaptive Metric Dimensionality Reduction—0
Passive Learning with Target Risk—0
Confusion-Based Online Learning and a Passive-Aggressive Scheme—0
Learning Multiple Tasks using Shared Hypotheses—0
Robustness and Generalization for Metric Learning—0
Algorithms for Learning Kernels Based on Centered Alignment—0
Generalization Bounds and Consistency for Latent Structural Probit and Ramp Loss—0
Non-parametric Group Orthogonal Matching Pursuit for Sparse Learning with Multiple Kernels—0
The Impact of Unlabeled Patterns in Rademacher Complexity Theory for Kernel Classifiers—0
Maximum Margin Multi-Label Structured Prediction—0
On Combining Machine Learning with Decision Making—0
Survey & Experiment: Towards the Learning Accuracy—0
Semi-Supervised Learning with Adversarially Missing Label Information—0
Co-regularization Based Semi-supervised Domain Adaptation—0
Two-Layer Generalization Analysis for Ranking Using Rademacher Average—0
Sufficient Conditions for Generating Group Level Sparsity in a Robust Minimax Framework—0
Human Rademacher Complexity—0
Domain Adaptation: Learning Bounds and Algorithms—0
Rademacher Complexity Bounds for Non-I.I.D. Processes—0
Stability Bounds for Non-i.i.d. Processes—0
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