Multi-task PPI framework uses cross-task recalibration to improve inference power across related tasks, with a proof that gains require nonlinear proxy-ground-truth structure, shown on synthetic data and a 2024 election LM audit case study.
Another look at inference after prediction
4 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 4representative citing papers
The MLA-UCB algorithm uses ML-generated surrogate rewards from auxiliary data to provably lower cumulative regret in multi-armed bandits, achieving asymptotic optimality under joint Gaussian assumptions without requiring knowledge of the true-surrogate covariance.
Adaptive Matrix Validation calibrates AI-mapped survey responses using sparse randomized validation questions from other respondents then corrects with the target's own answers, with estimators and planning formulas for means, subgroups, and regressions.
Introduces convolution smoothing of the check-loss for prediction-powered quantile regression, derives asymptotics under misspecification, and proposes an ensemble estimator.
citing papers explorer
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Multi-Armed Bandits With Machine Learning-Generated Surrogate Rewards
The MLA-UCB algorithm uses ML-generated surrogate rewards from auxiliary data to provably lower cumulative regret in multi-armed bandits, achieving asymptotic optimality under joint Gaussian assumptions without requiring knowledge of the true-surrogate covariance.