excess risk

Approximation Rates for Schrödinger Bridge Potentials via Fixed-Point ERM UAI 2026 Improving the Convergence of Private Shuffled Gradient Methods with Public Data UAI 2026 Learning Expressive Random Feature Models via Parametrized Activations UAI 2026 On the Gradient Complexity of Private Optimization with Private Oracles COLT 2026 Rademacher Complexity for Distributionally Robust Learning AAAI 2026 Risk Comparisons in Linear Regression: Implicit Regularization Dominates Explicit Regularization (Extended Abstract) COLT 2026 Stochastic Gradient Methods: Bias, Stability and Generalization JMLR 2026 A primer on linear classification with missing data AISTATS 2025 Benign Overfitting in Out-of-Distribution Generalization of Linear Models ICLR 2025 Decision from Suboptimal Classifiers: Excess Risk Pre- and Post-Calibration AISTATS 2025 Epistemic Uncertainty and Excess Risk in Variational Inference AISTATS 2025 Generalization Analysis for Supervised Contrastive Representation Learning under Non-IID Settings ICML 2025 Generalization Lower Bounds for GD and SGD in Smooth Stochastic Convex Optimization AISTATS 2025 Generalization Performance of Ensemble Clustering: From Theory to Algorithm ICML 2025 How Gradient descent balances features: A dynamical analysis for two-layer neural networks ICLR 2025 Learning Curves of Stochastic Gradient Descent in Kernel Regression ICML 2025 Sharper Guarantees for Learning Neural Network Classifiers with Gradient Methods ICLR 2025 Stability and Generalization Analysis of Decentralized SGD: Sharper Bounds Beyond Lipschitzness and Smoothness ICML 2025 The Size of Teachers as a Measure of Data Complexity: PAC-Bayes Excess Risk Bounds and Scaling Laws AISTATS 2025 Universal Rates of ERM for Agnostic Learning COLT 2025 Bounding the Excess Risk for Linear Models Trained on Marginal-Preserving, Differentially-Private, Synthetic Data ICML 2024 Classification of Data Generated by Gaussian Mixture Models Using Deep ReLU Networks JMLR 2024 Counterfactual Fairness by Combining Factual and Counterfactual Predictions NeurIPS 2024 Faster Algorithms for User-Level Private Stochastic Convex Optimization NeurIPS 2024 Fast Rates in Pool-Based Batch Active Learning JMLR 2024