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.
Active Statistical Inference
5 Pith papers cite this work. Polarity classification is still indexing.
5
Pith papers citing it
abstract
Inspired by the concept of active learning, we propose active inference$\unicode{x2013}$a methodology for statistical inference with machine-learning-assisted data collection. Assuming a budget on the number of labels that can be collected, the methodology uses a machine learning model to identify which data points would be most beneficial to label, thus effectively utilizing the budget. It operates on a simple yet powerful intuition: prioritize the collection of labels for data points where the model exhibits uncertainty, and rely on the model's predictions where it is confident. Active inference constructs provably valid confidence intervals and hypothesis tests while leveraging any black-box machine learning model and handling any data distribution. The key point is that it achieves the same level of accuracy with far fewer samples than existing baselines relying on non-adaptively-collected data. This means that for the same number of collected samples, active inference enables smaller confidence intervals and more powerful p-values. We evaluate active inference on datasets from public opinion research, census analysis, and proteomics.
representative citing papers
PPAT residualizes losses via a prediction-powered control variate inside LURE, yielding lower-variance unbiased risk estimates, tailored acquisition, and asymptotic CIs that cover with fewer labels.
GLIDE is a Python library that packages multiple PPI estimators and samplers for reliable GenAI evaluation and reports annotation savings in an agentic case study.
This review synthesizes representative advances in high-dimensional statistics, highlights common themes and open problems, and points to key entry works.
citing papers explorer
-
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.
-
Prediction-Powered Active Testing
PPAT residualizes losses via a prediction-powered control variate inside LURE, yielding lower-variance unbiased risk estimates, tailored acquisition, and asymptotic CIs that cover with fewer labels.
-
Industrializing Prediction-Powered Inference: The GLIDE Library for Reliable GenAI and Agentic Systems Evaluation
GLIDE is a Python library that packages multiple PPI estimators and samplers for reliable GenAI evaluation and reports annotation savings in an agentic case study.
-
High-Dimensional Statistics: Reflections on Progress and Open Problems
This review synthesizes representative advances in high-dimensional statistics, highlights common themes and open problems, and points to key entry works.
- Optimal Causal Annotations: An Application to Casenotes in Social Services