Stephen Bates

Robust Calibration with Multi-domain Temperature Scaling

2022 · arXiv (Cornell University)

Uncertainty quantification is essential for the reliable deployment of machine learning models to high-stakes application domains. Uncertainty quantification is all the more challenging when training distribution and test distribution are different, even the distribution shifts …

Sharp Results for Hypothesis Testing with Risk-Sensitive Agents

2024 · arXiv (Cornell University)

Statistical protocols are often used for decision-making involving multiple parties, each with their own incentives, private information, and ability to influence the distributional properties of the data. We study a game-theoretic version of hypothesis testing …

Prediction-Powered Inference with Imputed Covariates and Nonuniform Sampling

2025 · arXiv (Cornell University)

Machine learning models are increasingly used to produce predictions that serve as input data in subsequent statistical analyses. For example, computer vision predictions of economic and environmental indicators based on satellite imagery are used in …