empirical risk

Generalizability of Neural Networks Minimizing Empirical Risk Based on Expressive Power ICLR 2025 Generalization and Robustness of the Tilted Empirical Risk ICML 2025 Generalization Analysis of Machine Learning Algorithms via the Worst-Case Data-Generating Probability Measure AAAI 2024 How to Escape Sharp Minima with Random Perturbations ICML 2024 Learning via Surrogate PAC-Bayes NeurIPS 2024 On the Concentration of the Minimizers of Empirical Risks JMLR 2024 Data Pruning via Moving-one-Sample-out NeurIPS 2023 FlatMatch: Bridging Labeled Data and Unlabeled Data with Cross-Sharpness for Semi-Supervised Learning NeurIPS 2023 On Solution Functions of Optimization: Universal Approximation and Covering Number Bounds AAAI 2023 PGFed: Personalize Each Client's Global Objective for Federated Learning ICCV 2023 Statistical Robustness of Empirical Risks in Machine Learning JMLR 2023 A Wasserstein Distance Approach for Concentration of Empirical Risk Estimates JMLR 2022 Benign Underfitting of Stochastic Gradient Descent NeurIPS 2022 Calibrated Data-Dependent Constraints with Exact Satisfaction Guarantees NeurIPS 2022 Domain Generalization without Excess Empirical Risk NeurIPS 2022 PACm-Bayes: Narrowing the Empirical Risk Gap in the Misspecified Bayesian Regime AISTATS 2022 Private Convex Optimization via Exponential Mechanism COLT 2022 Private Isotonic Regression NeurIPS 2022 Discriminative Complementary-Label Learning with Weighted Loss ICML 2021 Learning with Gradient Descent and Weakly Convex Losses AISTATS 2021 Matrix Completion with Model-free Weighting ICML 2021 Private Stochastic Non-convex Optimization with Improved Utility Rates IJCAI 2021 Regularizing Neural Networks via Adversarial Model Perturbation CVPR 2021 Training Recurrent Neural Networks via Forward Propagation Through Time ICML 2021 Hierarchically Robust Representation Learning CVPR 2020