bias-variance decomposition

DeepBooTS: Dual-Stream Residual Boosting for Drift-Resilient Time-Series Forecasting AAAI 2026 Label-Wise uncertainty decomposition for Multi-label Classification by Maximizing Type II Likelihood UAI 2026 A Bias-Variance Decomposition for Ensembles over Multiple Synthetic Datasets AISTATS 2025 Bias-variance decompositions: the exclusive privilege of Bregman divergences JMLR 2025 Demystifying the Paradox of Importance Sampling with an Estimated History-Dependent Behavior Policy in Off-Policy Evaluation ICML 2025 High-Dimensional Kernel Methods under Covariate Shift: Data-Dependent Implicit Regularization ICML 2024 A Unified Theory of Diversity in Ensemble Learning JMLR 2023 On the Variance, Admissibility, and Stability of Empirical Risk Minimization NeurIPS 2023 Rethinking Semi-Supervised Imbalanced Node Classification from Bias-Variance Decomposition NeurIPS 2023 Uncertainty Estimates of Predictions via a General Bias-Variance Decomposition AISTATS 2023 Bias-Variance Decompositions for Margin Losses AISTATS 2022 Diverse Weight Averaging for Out-of-Distribution Generalization NeurIPS 2022 Leveling Down in Computer Vision: Pareto Inefficiencies in Fair Deep Classifiers CVPR 2022 Benign Overfitting of Constant-Stepsize SGD for Linear Regression COLT 2021 Comparing the Value of Labeled and Unlabeled Data in Method-of-Moments Latent Variable Estimation AISTATS 2021 Ensembling With a Fixed Parameter Budget: When Does It Help and Why? ACML 2021 Kernel regression in high dimensions: Refined analysis beyond double descent AISTATS 2021 The statistical advantage of automatic NLG metrics at the system level ACL 2021 Double Trouble in Double Descent: Bias and Variance(s) in the Lazy Regime ICML 2020 Understanding Double Descent Requires A Fine-Grained Bias-Variance Decomposition NeurIPS 2020 Beyond Least-Squares: Fast Rates for Regularized Empirical Risk Minimization through Self-Concordance COLT 2019 Why Is My Classifier Discriminatory? NeurIPS 2018 Bias-Variance Analysis of Support Vector Machines for the Development of SVM-Based Ensemble Methods JMLR 2004