empirical risk minimization

From Inverse Optimization to Feasibility to ERM ICML 2024 Generalized Tensor Decomposition for Understanding Multi-Output Regression under Combinatorial Shifts NeurIPS 2024 Is Efficient PAC Learning Possible with an Oracle That Responds "Yes" or "No"? COLT 2024 Label Noise: Ignorance Is Bliss NeurIPS 2024 Learning with a linear loss function: excess risk and estimation bounds for ERM, minmax MOM and their regularized versions with applications to robustness in sparse PCA. JMLR 2024 List Sample Compression and Uniform Convergence COLT 2024 Lost Domain Generalization Is a Natural Consequence of Lack of Training Domains AAAI 2024 Majority-of-Three: The Simplest Optimal Learner? COLT 2024 Mitigating Covariate Shift in Misspecified Regression with Applications to Reinforcement Learning COLT 2024 Mitigating Spurious Correlations via Disagreement Probability NeurIPS 2024 On Convex Optimization with Semi-Sensitive Features COLT 2024 On the Asymptotic Distribution of the Minimum Empirical Risk ICML 2024 On the Efficiency of ERM in Feature Learning NeurIPS 2024 On the Performance of Empirical Risk Minimization with Smoothed Data COLT 2024 On the Provable Advantage of Unsupervised Pretraining ICLR 2024 On the Stability and Generalization of Meta-Learning NeurIPS 2024 Open Problem: Can Local Regularization Learn All Multiclass Problems? COLT 2024 Optimal Multi-Distribution Learning COLT 2024 Optimistic Rates for Learning from Label Proportions COLT 2024 Prospective Learning: Learning for a Dynamic Future NeurIPS 2024 Regularization and Optimal Multiclass Learning COLT 2024 Sample-Efficient Agnostic Boosting NeurIPS 2024 Scaling laws for learning with real and surrogate data NeurIPS 2024 Set Learning for Accurate and Calibrated Models ICLR 2024 Sharp error bounds for imbalanced classification: how many examples in the minority class? AISTATS 2024