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Learning-to-Defer in Non-Stationary Time Series via Switching State-Space Models Variance Reduction for Expectations with Diffusion Teachers TASTE: A Designer-Annotated Multi-Dimensional Preference Dataset for AI-Generated Graphic Design Everywhere Valid Bounds on False Discovery Proportions in Conformal Inference Decision-Path Patterns as Tree Reliability Signals: Path-based Adaptive Weighting for Random Forest Classification The General Theory of Localization Methods CASCADE Conformal Prediction: Uncertainty-Adaptive Prediction Intervals for Two-Stage Clinical Decision Support Symmetrization of Loss Functions for Robust Training of Neural Networks in the Presence of Noisy Labels Tail Annealing for Heavy-Tailed Flow Matching Variance-Reduced Manifold Sampling via Polynomial-Maximization Density Estimation Latent Laplace Diffusion for Irregular Multivariate Time Series Precision Physical Activity Prescription via Reinforcement Learning for Functional Actions Reducing Diffusion Model Memorization with Higher Order Langevin Dynamics Provably Data-driven Lagrangian Relaxation for Mixed Integer Linear Programming Can Adaptive Gradient Methods Converge under Heavy-Tailed Noise? 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AutoEval Done Right: Using Synthetic Data for Model Evaluation
Pierre Boyeau, Anastasios N. Angelopoulos, Nir Yosef, Jitendra M · 2024-03-09 · via stat updates on arXiv.org

The evaluation of machine learning models using human-labeled validation data can be expensive and time-consuming. AI-labeled synthetic data can be used to decrease the number of human annotations required for this purpose in a process called autoevaluation. We suggest efficient and statistically principled algorithms for this purpose that improve sample efficiency while remaining unbiased. These algorithms increase the effective human-labeled sample size by up to 50% on experiments with GPT-4.