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.
Demystifying Prediction Powered Inference
arXiv:2601. 20819v2 Announce Type: replace-cross Abstract: Machine learning predictions are increasingly used to supplement incomplete or costly-to-measure outcomes in fields such as biomedical research, environmental science, and social science.
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arXiv AI
Jun 2
A Practical Upper Bound on Selection Bias Effects in Medical Prediction Models
arXiv:2606. 00563v1 Announce Type: cross Abstract: Selection bias is a common and often unavoidable aspect of real-world data that challenges the generalizability of machine learning models.
arXiv AI
Jul 23
Generative Augmented Inference of LLM-generated Data for Market Research: Theory and Empirical Evidence
arXiv:2604. 14575v3 Announce Type: replace-cross Abstract: Marketing research often relies on parameters estimated from costly human-generated data, such as conjoint survey responses, purchase decisions, and field experiment outcomes.
arXiv Machine Learning
Jun 26
No Free Lunch: Non-Asymptotic Analysis of Prediction-Powered Inference
arXiv:2505. 20178v2 Announce Type: replace-cross Abstract: Prediction-Powered Inference (PPI) is a popular strategy for combining gold-standard and possibly noisy pseudo-labels to perform statistical estimation.
arXiv Machine Learning
Jun 5
Industrializing Prediction-Powered Inference: The GLIDE Library for Reliable GenAI and Agentic Systems Evaluation
arXiv:2605. 31278v2 Announce Type: replace-cross Abstract: Reliable evaluation of agentic systems requires unbiased estimates with valid uncertainty, but standard practice navigates between costly human annotation and biased LLM-as-judge proxies.
arXiv Machine Learning
Aug 3
Analytical and Bootstrap Confidence Intervals of Double Machine Learning: Simulation studies and an application to rural-urban difference in obesity prevalence
arXiv:2607. 29456v1 Announce Type: cross Abstract: Double Machine Learning (DML) is a popular approach for treatment effect estimation in various settings, which allows a wide range of flexible machine learning methods to be used for nuisance parameter estimation while preserving valid inference.
Hugging Face Trending Papers
Jul 9
Prediction-Powered Active Testing
Active testing provides a label--efficient approach to risk estimation by adaptively selecting which test points should be labelled. However, existing estimators fail to exploit the informative predictions of powerful black--box models, even though such predictions are increasingly available in settings where labels remain expensive.
arXiv Machine Learning
Jun 15
Beyond the Training Distribution: Evaluating Predictions Under Distribution Shift and Selection Bias
arXiv:2606. 14506v1 Announce Type: cross Abstract: Understanding how a prediction model will perform in a new environment before deployment is essential to preventing harm when algorithms inform decision-making.
arXiv AI
Jun 3
Aligning Data-Driven Predictors with Allocation: A Decision-Focused Approach to Survival Analysis
arXiv:2606. 02671v1 Announce Type: cross Abstract: Machine learning predictors have become essential tools for guiding automated decision making.
arXiv AI
Jul 14
CDFM: Towards a General-Purpose Causal Discovery Foundation Model
arXiv:2607. 11508v1 Announce Type: cross Abstract: Causal discovery, the process of recovering underlying causal structures from observational data, is a fundamental pursuit across scientific disciplines.
arXiv Machine Learning
Aug 11
Integrating RCTs, RWD, AI/ML and Statistics: Next-Generation Evidence Synthesis
arXiv:2511. 19735v2 Announce Type: replace-cross Abstract: Randomized controlled trials (RCTs)have been the cornerstone of clinical evidence; however, their cost, duration, and restrictive eligibility criteria limit power and external validity.
arXiv Machine Learning
Aug 4
Information-Theoretic Foundations for Machine Learning
arXiv:2407. 12288v5 Announce Type: replace-cross Abstract: The progress of machine learning over the past decade is undeniable.