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Generalization Bounds

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TitleStatusHype
Two-Layer Generalization Analysis for Ranking Using Rademacher Average—0
Two Regimes of Generalization for Non-Linear Metric Learning—0
Understanding Adversarially Robust Generalization via Weight-Curvature Index—0
Understanding Augmentation-based Self-Supervised Representation Learning via RKHS Approximation and Regression—0
Understanding Difficult-to-learn Examples in Contrastive Learning: A Theoretical Framework for Spectral Contrastive Learning—0
Understanding Generalization in Recurrent Neural Networks—0
Understanding prompt engineering may not require rethinking generalization—0
Understanding the Generalization Error of Markov algorithms through Poissonization—0
Unified PAC-Bayesian Study of Pessimism for Offline Policy Learning with Regularized Importance Sampling—0
Generalization Bounds of Nonconvex-(Strongly)-Concave Stochastic Minimax Optimization—0
Uniform Convergence of Interpolators: Gaussian Width, Norm Bounds, and Benign Overfitting—0
Uniform Convergence of Interpolators: Gaussian Width, Norm Bounds and Benign Overfitting—0
Uniform Generalization Bound on Time and Inverse Temperature for Gradient Descent Algorithm and its Application to Analysis of Simulated Annealing—0
Uniform Generalization Bounds for Overparameterized Neural Networks—0
Uniform Generalization Bounds on Data-Dependent Hypothesis Sets via PAC-Bayesian Theory on Random Sets—0
Uniform-in-Time Wasserstein Stability Bounds for (Noisy) Stochastic Gradient Descent—0
Unlocking Tokens as Data Points for Generalization Bounds on Larger Language Models—0
VC-Dimension Based Generalization Bounds for Relational Learning—0
VC Theoretical Explanation of Double Descent—0
Weighted Distance Nearest Neighbor Condensing—0
What is a Good Metric to Study Generalization of Minimax Learners?—0
When and why are log-linear models self-normalizing?—0
When Invariant Representation Learning Meets Label Shift: Insufficiency and Theoretical Insights—0
Which Algorithms Have Tight Generalization Bounds?—0
Wide Neural Networks are Interpolating Kernel Methods: Impact of Initialization on Generalization—0
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