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Generalization Bounds

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TitleStatusHype
Generalization Analysis for Deep Contrastive Representation Learning—0
Generalization Guarantee of SGD for Pairwise Learning—0
Compression Implies Generalization—0
Generalization Guarantees for Multi-View Representation Learning and Application to Regularization via Gaussian Product Mixture Prior—0
Generalization Analysis for Contrastive Representation Learning under Non-IID Settings—0
Generalization Analysis for Contrastive Representation Learning—0
Compressing Heavy-Tailed Weight Matrices for Non-Vacuous Generalization Bounds—0
Generalizability of Neural Networks Minimizing Empirical Risk Based on Expressive Ability—0
Functions with average smoothness: structure, algorithms, and learning—0
Complex-valued embeddings of generic proximity data—0
A PAC-Bayesian Approach to Generalization Bounds for Graph Neural Networks—0
Aggregation Weighting of Federated Learning via Generalization Bound Estimation—0
Adaptive Data Analysis for Growing Data—0
A Communication-efficient Algorithm with Linear Convergence for Federated Minimax Learning—0
From Mutual Information to Expected Dynamics: New Generalization Bounds for Heavy-Tailed SGD—0
Fractal Structure and Generalization Properties of Stochastic Optimization Algorithms—0
Foundations of Coupled Nonlinear Dimensionality Reduction—0
Generalization Bounds with Minimal Dependency on Hypothesis Class via Distributionally Robust Optimization—0
Formal limitations of sample-wise information-theoretic generalization bounds—0
Cold Posteriors through PAC-Bayes—0
Fine-grained Generalization Analysis of Structured Output Prediction—0
Fine-grained Generalization Analysis of Vector-valued Learning—0
Coarse-Refinement Dilemma: On Generalization Bounds for Data Clustering—0
A Novel Plug-and-Play Approach for Adversarially Robust Generalization—0
A Generalized Doubly Robust Learning Framework for Debiasing Post-Click Conversion Rate Prediction—0
Fine-Grained Dynamic Framework for Bias-Variance Joint Optimization on Data Missing Not at Random—0
Fine-Grained Analysis of Stability and Generalization for Stochastic Gradient Descent—0
On the Sample Complexity of Stability Constrained Imitation Learning—0
Federated Variational Inference: Towards Improved Personalization and Generalization—0
Advocating for the Silent: Enhancing Federated Generalization for Non-Participating Clients—0
Class-wise Generalization Error: an Information-Theoretic Analysis—0
A Note on High-Probability versus In-Expectation Guarantees of Generalization Bounds in Machine Learning—0
f-Domain-Adversarial Learning: Theory and Algorithms for Unsupervised Domain Adaptation with Neural Networks—0
Classification with Deep Neural Networks and Logistic Loss—0
Fast Rate Bounds for Multi-Task and Meta-Learning with Different Sample Sizes—0
Characterizing Membership Privacy in Stochastic Gradient Langevin Dynamics—0
A note on generalization bounds for losses with finite moments—0
Generalization Error Bounds of Gradient Descent for Learning Over-parameterized Deep ReLU Networks—0
Fast Convergence in Learning Two-Layer Neural Networks with Separable Data—0
Fantastic Generalization Measures are Nowhere to be Found—0
Fair Off-Policy Learning from Observational Data—0
Fairness Overfitting in Machine Learning: An Information-Theoretic Perspective—0
Chaining Mutual Information and Tightening Generalization Bounds—0
A Nonlinear Kernel Support Matrix Machine for Matrix Learning—0
Fairness-aware Federated Learning—0
Failures of model-dependent generalization bounds for least-norm interpolation—0
Causal Dynamic Variational Autoencoder for Counterfactual Regression in Longitudinal Data—0
Extrapolation and Spectral Bias of Neural Nets with Hadamard Product: a Polynomial Net Study—0
An Information-Theoretic Framework for Out-of-Distribution Generalization with Applications to Stochastic Gradient Langevin Dynamics—0
A Generalization Bound of Deep Neural Networks for Dependent Data—0
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