empirical risk minimizer

Data Selection for ERMs COLT 2025 The Dimension Strikes Back with Gradients: Generalization of Gradient Methods in Stochastic Convex Optimization ALT 2025 Sharp Rates in Dependent Learning Theory: Avoiding Sample Size Deflation for the Square Loss ICML 2024 The sample complexity of ERMs in stochastic convex optimization AISTATS 2024 Privately Publishable Per-instance Privacy NeurIPS 2021 Learning the Valuations of a k-demand Agent ICML 2020 Robust high dimensional learning for Lipschitz and convex losses JMLR 2020 Does Data Augmentation Lead to Positive Margin? ICML 2019 Classification from Pairwise Similarity and Unlabeled Data ICML 2018 Learners that Use Little Information ALT 2018 On Binary Classification in Extreme Regions NeurIPS 2018 Learning with Square Loss: Localization through Offset Rademacher Complexity COLT 2015 Online and Stochastic Gradient Methods for Non-decomposable Loss Functions NeurIPS 2014