early stopping

Relating Adversarially Robust Generalization to Flat Minima ICCV 2021 Robust early-learning: Hindering the memorization of noisy labels ICLR 2021 Stability and Generalization for Randomized Coordinate Descent IJCAI 2021 Understanding and Improving Early Stopping for Learning with Noisy Labels NeurIPS 2021 Bounding the expected run-time of nonconvex optimization with early stopping UAI 2020 Denoising and Regularization via Exploiting the Structural Bias of Convolutional Generators ICLR 2020 Gradient Descent with Early Stopping is Provably Robust to Label Noise for Overparameterized Neural Networks AISTATS 2020 Greedy Convex Ensemble IJCAI 2020 Learning to Rank Learning Curves ICML 2020 Overfitting in adversarially robust deep learning ICML 2020 The Statistical Complexity of Early-Stopped Mirror Descent NeurIPS 2020 A Continuous-Time View of Early Stopping for Least Squares Regression AISTATS 2019 Bayesian Optimization Meets Bayesian Optimal Stopping ICML 2019 Boosted Kernel Ridge Regression: Optimal Learning Rates and Early Stopping JMLR 2019 Early-Stopping of Scattering Pattern Observation with Bayesian Modeling AAAI 2019 Faster Boosting with Smaller Memory NeurIPS 2019 Early Stopping for Nonparametric Testing NeurIPS 2018 Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization JMLR 2018 The Dynamics of Learning: A Random Matrix Approach ICML 2018 Early stopping for kernel boosting algorithms: A general analysis with localized complexities NeurIPS 2017 Optimal Rates for Multi-pass Stochastic Gradient Methods JMLR 2017 Early Stopping as Nonparametric Variational Inference AISTATS 2016 Iterative Regularization for Learning with Convex Loss Functions JMLR 2016 NYTRO: When Subsampling Meets Early Stopping AISTATS 2016 Optimal Learning for Multi-pass Stochastic Gradient Methods NeurIPS 2016