learning curve

Biasless Language Models Learn Unnaturally: How LLMs Fail to Distinguish the Possible from the Impossible EACL 2026 DABO: Difficulty-Aware Bayesian Optimization with Diffusion-Learned Priors CVPR 2026 A Novel Convex Gaussian Min Max Theorem for Repeated Features AISTATS 2025 Learning curves theory for hierarchically compositional data with power-law distributed features ICML 2025 To Label or Not to Label: PALM - A Predictive Model for Evaluating Sample Efficiency in Active Learning Models ICCV 2025 Universal Rates of ERM for Agnostic Learning COLT 2025 A Comprehensive Analysis on the Learning Curve in Kernel Ridge Regression NeurIPS 2024 Universal Rates for Active Learning NeurIPS 2024 Universal Rates of Empirical Risk Minimization NeurIPS 2024 A Theoretical Analysis of the Learning Dynamics under Class Imbalance ICML 2023 Efficient Bayesian Learning Curve Extrapolation using Prior-Data Fitted Networks NeurIPS 2023 Fine-Grained Distribution-Dependent Learning Curves COLT 2023 Learning Curves for Deep Structured Gaussian Feature Models NeurIPS 2023 Learning Curves for Noisy Heterogeneous Feature-Subsampled Ridge Ensembles NeurIPS 2023 On the Asymptotic Learning Curves of Kernel Ridge Regression under Power-law Decay NeurIPS 2023 Random Features Model with General Convex Regularization: A Fine Grained Analysis with Precise Asymptotic Learning Curves AISTATS 2023 Scaling Laws for Hyperparameter Optimization NeurIPS 2023 Universal Rates for Multiclass Learning COLT 2023 Learning Curves for Gaussian Process Regression with Power-Law Priors and Targets ICLR 2022 Learning Curves for SGD on Structured Features ICLR 2022 Precise Learning Curves and Higher-Order Scalings for Dot-product Kernel Regression NeurIPS 2022 Revisiting Neural Scaling Laws in Language and Vision NeurIPS 2022 Supervising the Multi-Fidelity Race of Hyperparameter Configurations NeurIPS 2022 Universal Rates for Interactive Learning NeurIPS 2022 Learning Curves for Analysis of Deep Networks ICML 2021