Robust Sampling for Active Statistical Inference

• URL: http://arxiv.org/abs/2511.08991v1
• Date: Thu, 13 Nov 2025 01:24:28 GMT
• Title: Robust Sampling for Active Statistical Inference
• Authors: Puheng Li, Tijana Zrnic, Emmanuel Candès,
• Abstract summary: Active statistical inference is a new method for inference with AI-assisted data collection.<n>We present robust sampling strategies for active statistical inference.<n>We demonstrate the utility of the method on a series of real datasets.
• Score: 11.929391566298841
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Active statistical inference is a new method for inference with AI-assisted data collection. Given a budget on the number of labeled data points that can be collected and assuming access to an AI predictive model, the basic idea is to improve estimation accuracy by prioritizing the collection of labels where the model is most uncertain. The drawback, however, is that inaccurate uncertainty estimates can make active sampling produce highly noisy results, potentially worse than those from naive uniform sampling. In this work, we present robust sampling strategies for active statistical inference. Robust sampling ensures that the resulting estimator is never worse than the estimator using uniform sampling. Furthermore, with reliable uncertainty estimates, the estimator usually outperforms standard active inference. This is achieved by optimally interpolating between uniform and active sampling, depending on the quality of the uncertainty scores, and by using ideas from robust optimization. We demonstrate the utility of the method on a series of real datasets from computational social science and survey research.

Related papers

• Optimal Sampling for Generalized Linear Model under Measurement Constraint with Surrogate Variables [3.5903555216741405]
  In some cases, surrogate variables are accessible across the entire dataset and can serve as approximations to the true response variable.<n>We propose an optimal sampling strategy that effectively harnesses the available information from surrogate variables.
  arXiv  Detail & Related papers  (2025-01-01T22:41:52Z)
• A Framework for Efficient Model Evaluation through Stratification, Sampling, and Estimation [17.351089059392674]
  We propose a framework for model evaluation that includes stratification, sampling, and estimation components. We show that stratification via k-means clustering based on accurate predictions of model performance yields efficient estimators. We also find that model-assisted estimators, which leverage predictions of model accuracy on the unlabeled portion of the dataset, are generally more efficient than the traditional estimates.
  arXiv  Detail & Related papers  (2024-06-11T14:49:04Z)
• Stratified Prediction-Powered Inference for Hybrid Language Model Evaluation [62.2436697657307]
  Prediction-powered inference (PPI) is a method that improves statistical estimates based on limited human-labeled data.<n>We propose a method called Stratified Prediction-Powered Inference (StratPPI)<n>We show that the basic PPI estimates can be considerably improved by employing simple data stratification strategies.
  arXiv  Detail & Related papers  (2024-06-06T17:37:39Z)
• Uncertainty for Active Learning on Graphs [70.44714133412592]
  Uncertainty Sampling is an Active Learning strategy that aims to improve the data efficiency of machine learning models.<n>We benchmark Uncertainty Sampling beyond predictive uncertainty and highlight a significant performance gap to other Active Learning strategies.<n>We develop ground-truth Bayesian uncertainty estimates in terms of the data generating process and prove their effectiveness in guiding Uncertainty Sampling toward optimal queries.
  arXiv  Detail & Related papers  (2024-05-02T16:50:47Z)
• Active Statistical Inference [14.00987234726578]
  methodology uses a machine learning model to identify which data points are most beneficial to label. It achieves the same level of accuracy with far fewer samples than existing baselines.
  arXiv  Detail & Related papers  (2024-03-05T18:46:50Z)
• Uncertainty-Aware Instance Reweighting for Off-Policy Learning [63.31923483172859]
  We propose a Uncertainty-aware Inverse Propensity Score estimator (UIPS) for improved off-policy learning. Experiment results on synthetic and three real-world recommendation datasets demonstrate the advantageous sample efficiency of the proposed UIPS estimator.
  arXiv  Detail & Related papers  (2023-03-11T11:42:26Z)
• ZigZag: Universal Sampling-free Uncertainty Estimation Through Two-Step Inference [54.17205151960878]
  We introduce a sampling-free approach that is generic and easy to deploy. We produce reliable uncertainty estimates on par with state-of-the-art methods at a significantly lower computational cost.
  arXiv  Detail & Related papers  (2022-11-21T13:23:09Z)
• Statistical Inference After Adaptive Sampling for Longitudinal Data [9.468593929311867]
  We develop novel methods to perform a variety of statistical analyses on adaptively sampled data via Z-estimation. We develop novel theoretical tools for empirical processes on non-i.i.d., adaptively sampled longitudinal data which may be of independent interest.
  arXiv  Detail & Related papers  (2022-02-14T23:48:13Z)
• Leveraging Unlabeled Data to Predict Out-of-Distribution Performance [63.740181251997306]
  Real-world machine learning deployments are characterized by mismatches between the source (training) and target (test) distributions. In this work, we investigate methods for predicting the target domain accuracy using only labeled source data and unlabeled target data. We propose Average Thresholded Confidence (ATC), a practical method that learns a threshold on the model's confidence, predicting accuracy as the fraction of unlabeled examples.
  arXiv  Detail & Related papers  (2022-01-11T23:01:12Z)
• Non-asymptotic analysis and inference for an outlyingness induced winsorized mean [0.0]
  This article investigates the robustness of leading sub-Gaussian estimators of mean. It reveals that none of them can resist greater than $25%$ contamination in data. It also introduces an outlyingness induced winsorized mean which has the best possible robustness.
  arXiv  Detail & Related papers  (2021-05-05T21:35:24Z)
• Robust Validation: Confident Predictions Even When Distributions Shift [19.327409270934474]
  We describe procedures for robust predictive inference, where a model provides uncertainty estimates on its predictions rather than point predictions. We present a method that produces prediction sets (almost exactly) giving the right coverage level for any test distribution in an $f$-divergence ball around the training population. An essential component of our methodology is to estimate the amount of expected future data shift and build robustness to it.
  arXiv  Detail & Related papers  (2020-08-10T17:09:16Z)
• Efficient Ensemble Model Generation for Uncertainty Estimation with Bayesian Approximation in Segmentation [74.06904875527556]
  We propose a generic and efficient segmentation framework to construct ensemble segmentation models. In the proposed method, ensemble models can be efficiently generated by using the layer selection method. We also devise a new pixel-wise uncertainty loss, which improves the predictive performance.
  arXiv  Detail & Related papers  (2020-05-21T16:08:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.