Prediction-Powered Inference

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6 papers in the last four weeks, against 1 the four weeks before. 0.1% of all new papers.

Jul 13Week of Sep 28

Latest papers 20

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Prediction-powered inference for time series across space
Oct 6, 2026Shahzar Rizvi, David Burt, Vishwak Srinivasan +3Spatiotemporal Forecasting Prediction-Powered Inference
MIT LIDS · UMass Amherst · Stanford University +1
Prediction-powered Neural Architecture Search
Oct 1, 2026Pascal Janetzky, Yuxin Wang, Michael Klar +1Neural Architecture Search Prediction-Powered Inference
Bosch Center for Artificial Intelligence · LMU Munich Munich Center for Machine Learning(MCML)
ABC-Align: Prediction-Powered Alignment with Adaptive Bias Control
Sep 28, 2026Eric Frankel, Banghua Zhu, Sewoong Oh +1RL for Language Models LLM Alignment
University of Washington, Seattle
Transfer Calibrated Prediction Powered Inference
Sep 28, 2026Aditya T. Vadlamani, Jae Ho Chang, Srinivasan Parthasarathy +1Domain Adaptation Prediction-Powered Inference
The Ohio State University · Yale University
How Synthetic Labels Improve Conformal Prediction: A Perspective on Conditional Coverage
Sep 27, 2026Qianyi Chen, Bo LiQuantile Regression Conformal Prediction
School of Economics and Management, Tsinghua University
Augmented Hypothesis Testing with Persona-Based LLM Simulations
Sep 21, 2026Ziyad Benomar, Aymen Al Marjani, Paul Missault +1A/b Testing Learning-Augmented Algorithms
Amazon Luxembourg, Luxembourg · Amazon Barcelona, Spain
Prediction-Powered Smoothing and Validation for Disaggregated AI Evaluation
Sep 17, 2026Sho Kawano, Zehang Richard Li, Paul A. ParkerAI Agent Evaluation Prediction-Powered Inference
Department of Statistics, University of California, Santa Cruz, CA, USA
Calibrating Small Language Models for Claim Check-Worthiness Detection
Aug 31, 2026Pratuat Amatya, V Venktesh, Vinay SettyClaim Verification Small Language Models
1Factiverse AS · University of Stavanger · 3Stockholm University
Prediction-Powered Active Testing
Jul 9, 2026Kianoosh Ashouritaklimi, Valentin Kilian, Daolang Huang +2Confidence Region Estimation Variance Reduction
Department of Statistics, University of Oxford · Department of Computer Science, Aalto University
Prediction-Powered Causal Inference by Automatic Debiased Machine Learning and Semi-Supervised Riesz Regression
Jun 11, 2026Masahiro KatoSemiparametric Inference Parameter Estimation
Data Analytics Department, Mizuho-DL Financial Technology, Co., Ltd.
Statistically Reliable LLM-Based Ranking Evaluation via Prediction-Powered Inference
Jun 3, 2026Abhishek DivekarLLM-as-a-Judge IR Evaluation
Amazon
Industrializing Prediction-Powered Inference: The GLIDE Library for Reliable GenAI and Agentic Systems Evaluation
May 29, 2026Grégoire Martinon, Ibrahim Merad, Mohammed RakiLLM-Assisted Annotation LLM Agent Evaluation
Emerton Data, 16 Avenue Hoche, 75008, Paris, France.
Prediction-Powered Inference Across Many Tasks for AI Evaluation & Social Science Research
May 28, 2026Nicolas Emmenegger, Ellery Stahler, Chara PodimataConfidence Region Estimation Uncertainty Quantification
MIT Cambridge, MA
Active Multiple-Prediction-Powered Inference
May 8, 2026Nicholas Brawand, Nima Leclerc, Anhthy Ngo +4Adaptive Inference Cost-Aware Inference
The MITRE Corporation · Georgia Institute of Technology
Prediction-powered Inference by Mixture of Experts
Apr 30, 2026Yanwu Gu, Linglong Kong, Dong XiaUncertainty Quantification Mixture-of-Experts Models
Department of Mathematics, Hong Kong University of Science and Technology · Department of Mathematical and Statistical Sciences, University of Alberta
Calibeating Prediction-Powered Inference
Apr 23, 2026Lars van der Laan, Mark Van Der LaanInverse Probability Weighting Parameter Estimation
Department of Statistics, University of Washington · Department of Statistics and Division of Biostatistics, Center for Targeted Machine Learning and Causal Inference, University of California, Berkeley
Revisiting Active Sequential Prediction-Powered Mean Estimation
Apr 20, 2026Maria-Eleni Sfyraki, Jun-Kun WangParameter Estimation Active Learning
University of California San Diego
Rectification Difficulty and Optimal Sample Allocation in LLM-Augmented Surveys
Apr 19, 2026Zikun Ye, Hema YoganarasimhanPrediction-Powered Inference
University of Washington
No Free Lunch: Non-Asymptotic Analysis of Prediction-Powered Inference
May 26, 2025Pranav Mani, Peng Xu, Zachary C. Lipton +1Adaptive Inference Statistical Learning Theory
Abridge AI, San Francisco, CA, USA · Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA, USA · Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
Predictions as Surrogates: Revisiting Surrogate Outcomes in the Age of AI
Jan 16, 2025Wenlong Ji, Lihua Lei, Tijana ZrnicEfficient Inference Prediction-Powered Inference
Stanford University

Oct 6, 2026, stat.MEJ/K move · Enter open · S save

Prediction-powered inference for time series across space

Shahzar Rizvi, David Burt, Vishwak Srinivasan, Renato Berlinghieri+2

MIT LIDS · UMass Amherst · Stanford University+1

Spatiotemporal Forecasting Prediction-Powered Inference Robust Estimation

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The following motif is common in spatiotemporal settings: we have a sequence of covariate and label pairs observed for a relatively short, recent time period. We have access to unlabeled covariates over a longer time period. Data is observed over many spatial locations. For instance, crop yield might be observed over a large geographical area for recent years, but weather data (which is informative about crop yield) is available for a much longer period. The goal is to estimate, at each spatial location, the expected label (e.g., crop yield) in the future and provide a valid confidence interval for this value. The observed time period alone is too short for reliable estimates. Imputing missing labels with machine learning can cause substantial bias. Prediction-powered inference (PPI) can correct for this bias, but it relies on an i.i.d. assumption that breaks under our expected temporal dependencies. Heteroskedasticity and autocorrelation consistent (HAC) procedures account for temporal correlation, but have not been adapted to cases where some labels are imputed. We provide reliable point estimates and confidence intervals given: short labeled time series (across spatial locations), a longer unlabeled time series, and an imperfect predictor of labels given covariates. We show our method outperforms natural alternatives.

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