← Back to all news
arXiv Machine Learning By Erik Skalnes, Michael Oberst

Fixed-Size Active Statistical Inference

Read the original on arXiv Machine Learning →

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

Related stories

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
llmssafety
More like this →
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
More like this →
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
More like this →