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Generalization Bounds

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Showing 601–650 of 686 papers

TitleStatusHype
Improving Generalization Bounds for VC Classes Using the Hypergeometric Tail InversionCode0
Dichotomize and Generalize: PAC-Bayesian Binary Activated Deep Neural NetworksCode0
Generalization Bound and New Algorithm for Clean-Label Backdoor AttackCode0
A path-norm toolkit for modern networks: consequences, promises and challengesCode0
Transfer Learning via Minimizing the Performance Gap Between DomainsCode0
Transformed Low-Rank Parameterization Can Help Robust Generalization for Tensor Neural NetworksCode0
Regularization via Mass TransportationCode0
Information-Theoretic Generalization Bounds for SGLD via Data-Dependent EstimatesCode0
Computing Nonvacuous Generalization Bounds for Deep (Stochastic) Neural Networks with Many More Parameters than Training DataCode0
Generalization Bounds for Causal Regression: Insights, Guarantees and Sensitivity AnalysisCode0
Stability and Generalization in Free Adversarial TrainingCode0
Information-theoretic generalization bounds for black-box learning algorithmsCode0
Information-Theoretic Characterization of the Generalization Error for Iterative Semi-Supervised LearningCode0
Transformers as Algorithms: Generalization and Stability in In-context LearningCode0
Sequence Length Independent Norm-Based Generalization Bounds for TransformersCode0
Graph Representational Learning: When Does More Expressivity Hurt Generalization?Code0
Uncertainty Calibration for Counterfactual Propensity Estimation in RecommendationCode0
A PAC-Bayesian Perspective on Structured Prediction with Implicit Loss EmbeddingsCode0
Stability and Generalization of Stochastic Gradient Methods for Minimax ProblemsCode0
Generalization bounds for graph convolutional neural networks via Rademacher complexityCode0
Diametrical Risk Minimization: Theory and ComputationsCode0
Generalization Bounds for Heavy-Tailed SDEs through the Fractional Fokker-Planck EquationCode0
Instance based Generalization in Reinforcement LearningCode0
PAC-Bayesian Adversarially Robust Generalization Bounds for Graph Neural NetworkCode0
Unveiling the Mechanisms of Explicit CoT Training: How CoT Enhances Reasoning GeneralizationCode0
Generalization Bounds for Learning with Linear, Polygonal, Quadratic and Conic Side KnowledgeCode0
Integral Probability Metrics PAC-Bayes BoundsCode0
Towards Understanding Generalization of Macro-AUC in Multi-label LearningCode0
Generalization Bounds For Meta-Learning: An Information-Theoretic AnalysisCode0
Random deep neural networks are biased towards simple functionsCode0
Comparing Comparators in Generalization BoundsCode0
PAC-Bayesian Generalization Bounds for Adversarial Generative ModelsCode0
Chaotic Regularization and Heavy-Tailed Limits for Deterministic Gradient DescentCode0
A PAC-Bayesian Framework for Optimal Control with Stability GuaranteesCode0
Theoretical Insights into Fine-Tuning Attention Mechanism: Generalization and OptimizationCode0
Learnability of Competitive Threshold ModelsCode0
Adversarial Transform Particle FiltersCode0
Learning Against Distributional Uncertainty: On the Trade-off Between Robustness and SpecificityCode0
Non-Vacuous Generalization Bounds at the ImageNet Scale: A PAC-Bayesian Compression ApproachCode0
Learning an Explicit Hyperparameter Prediction Function Conditioned on TasksCode0
Uniform convergence may be unable to explain generalization in deep learningCode0
Towards Size-Independent Generalization Bounds for Deep Operator NetsCode0
Robust Fine-Tuning of Deep Neural Networks with Hessian-based Generalization GuaranteesCode0
Robust Generalization despite Distribution Shift via Minimum Discriminating InformationCode0
Approximation and Learning with Deep Convolutional Models: a Kernel PerspectiveCode0
Generalization Bounds for Meta-Learning via PAC-Bayes and Uniform StabilityCode0
Learning Expressive Priors for Generalization and Uncertainty Estimation in Neural NetworksCode0
Rethinking Breiman's Dilemma in Neural Networks: Phase Transitions of Margin DynamicsCode0
A PAC-Bayesian Analysis of Randomized Learning with Application to Stochastic Gradient DescentCode0
On Cold Posteriors of Probabilistic Neural Networks: Understanding the Cold Posterior Effect and A New Way to Learn Cold Posteriors with Tight Generalization GuaranteesCode0
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