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arXiv Machine Learning

Fixed-Size Active Statistical Inference

The paper introduces Conditional Poisson Sampling (CPS) as a fixed-size alternative to Poisson Sampling (PS) for variance reduction in statistical inference. CPS can replace PS in multivariate Z-estimation problems, including convex M-estimation, without increasing asymptotic variance and often improving it when predictions are imperfect. The authors demonstrate these benefits on synthetic and real datasets and provide a Python package for CPS implementation.

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arXiv Machine Learning
Aug 31

Learning a Size-Weight Frontier for Synthetic-Augmented Inference

The paper introduces a framework for synthetic‑augmented inference that balances the number of synthetic observations with their assigned weight. It defines a size‑weight frontier, estimating for each weight the maximum synthetic sample size that still guarantees target task‑marginal coverage for all smaller sizes. The authors provide finite‑sample coverage guarantees for configurations on or below this frontier and demonstrate that, when applied to augment opinion survey data with large language model responses, the method achieves the desired coverage while significantly tightening confidence intervals.

By Chengpiao Huang, Kaizheng Wang
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arXiv Machine Learning
3d ago

Pro-Active Inference

The paper introduces Pro-Active Inference (PAI), a method for efficiently estimating a population mean when only a subset of outcomes can be observed. PAI proactively selects which observation to label next from a covariate pool, updating machine‑learning predictions online, and provides finite‑sample unbiasedness, asymptotic validity, and an improved variance estimator. Experiments on eight datasets show that PAI outperforms Sequential Active Inference in most settings, while diagnostics reveal when additional adaptivity is beneficial or detrimental.

By Skyler Wu, Yash Nair, Antoine Maechler, Emmanuel J. Cand\`es
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arXiv Machine Learning
Oct 7

Assumption-lean logistic regression with missing covariates

arXiv:2610.07292v1 Announce Type: cross Abstract: Missing covariates are frequently encountered in supervised learning problems, and classical methods for estimation using such data use carefully cho...

By Jyotishka Ray Choudhury, Kabir Aladin Verchand, Richard J. Samworth, Ashwin Pananjady
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arXiv Machine Learning
Jul 10

Prediction-Powered Active Testing

arXiv:2607. 08347v1 Announce Type: cross Abstract: Active testing provides a label--efficient approach to risk estimation by adaptively selecting which test points should be labelled.

By Kianoosh Ashouritaklimi, Valentin Kilian, Daolang Huang, Tom Rainforth, Fran\c{c}ois Caron
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