empirical risk minimization

RATT: Leveraging Unlabeled Data to Guarantee Generalization ICML 2021 Refined Learning Bounds for Kernel and Approximate k-Means NeurIPS 2021 Regularized ERM on random subspaces AISTATS 2021 Risk Minimization from Adaptively Collected Data: Guarantees for Supervised and Policy Learning NeurIPS 2021 Selecting Data Augmentation for Simulating Interventions ICML 2021 SGD Generalizes Better Than GD (And Regularization Doesn’t Help) COLT 2021 SGD: The Role of Implicit Regularization, Batch-size and Multiple-epochs NeurIPS 2021 SONIA: A Symmetric Blockwise Truncated Optimization Algorithm AISTATS 2021 Stable Adversarial Learning under Distributional Shifts AAAI 2021 Strategic Classification Made Practical ICML 2021 SWAD: Domain Generalization by Seeking Flat Minima NeurIPS 2021 Systematic generalisation with group invariant predictions ICLR 2021 The Risks of Invariant Risk Minimization ICLR 2021 A Continuous-Time Mirror Descent Approach to Sparse Phase Retrieval NeurIPS 2020 A Group-Theoretic Framework for Data Augmentation NeurIPS 2020 A Group-Theoretic Framework for Data Augmentation JMLR 2020 A One-step Approach to Covariate Shift Adaptation ACML 2020 Approximate Cross-Validation for Structured Models NeurIPS 2020 Approximate Cross-validation: Guarantees for Model Assessment and Selection AISTATS 2020 A Statistical Learning Approach to Modal Regression JMLR 2020 Communication-Efficient Distributed Optimization in Networks with Gradient Tracking and Variance Reduction JMLR 2020 Differentially Private Learning with Small Public Data AAAI 2020 Do Subsampled Newton Methods Work for High-Dimensional Data? AAAI 2020 Efficient Distributed Hessian Free Algorithm for Large-scale Empirical Risk Minimization via Accumulating Sample Strategy AISTATS 2020 Efficient improper learning for online logistic regression COLT 2020