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generalization guarantee

53 papers

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differential privacy (1016) generalization bound (652) empirical risk minimization (352) optimization algorithm (148) sample complexity (1169) domain adaptation (4595) binary classification (678) semi-supervised learning (2341) adaptive data analysis (10) learning to optimize (15)

Papers

Provably Data-Driven Projection Method for Quadratic Programming AAAI 2026
Data-Driven Performance Guarantees for Classical and Learned Optimizers JMLR 2025
Learning-to-Optimize with PAC-Bayesian Guarantees: Theoretical Considerations and Practical Implementation JMLR 2025
Sim-to-Lab-to-Real: Safe Reinforcement Learning with Shielding and Generalization Guarantees (Abstract Reprint) AAAI 2024
Public-data Assisted Private Stochastic Optimization: Power and Limitations NIPS 2024
Sample Boosting Algorithm (SamBA) - An interpretable greedy ensemble classifier based on local expertise for fat data UAI 2023
On the Generalization of Multi-modal Contrastive Learning ICML 2023
PAC-Bayesian Learning of Optimization Algorithms AISTATS 2023
Generalization Guarantees of Self-Training of Halfspaces under Label Noise Corruption IJCAI 2023
Principled Approaches for Private Adaptation from a Public Source AISTATS 2023
Exact Generalization Guarantees for (Regularized) Wasserstein Distributionally Robust Models NIPS 2023
Rethinking Label Refurbishment: Model Robustness under Label Noise AAAI 2023
Multi-Layer Neural Networks as Trainable Ladders of Hilbert Spaces ICML 2023
On Proper Learnability between Average- and Worst-case Robustness NIPS 2023
Minimum Description Length and Generalization Guarantees for Representation Learning NIPS 2023
Supervised Learning with General Risk Functionals ICML 2022
Robust Fine-Tuning of Deep Neural Networks with Hessian-based Generalization Guarantees ICML 2022
Generalization Guarantee of Training Graph Convolutional Networks with Graph Topology Sampling ICML 2022
Provably tuning the ElasticNet across instances NIPS 2022
PACm-Bayes: Narrowing the Empirical Risk Gap in the Misspecified Bayesian Regime AISTATS 2022
Adaptive Data Analysis with Correlated Observations ICML 2022
Identifying good directions to escape the NTK regime and efficiently learn low-degree plus sparse polynomials NIPS 2022
Graph Convolution Network based Recommender Systems: Learning Guarantee and Item Mixture Powered Strategy NIPS 2022
Open Problem: Better Differentially Private Learning Algorithms with Margin Guarantees COLT 2022
Pairwise Fairness for Ordinal Regression AISTATS 2022