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.
Inferential Evaluation of Surrogate-Derived Models under Covariate Shift
arXiv:2608. 15783v1 Announce Type: cross Abstract: In transfer-learning settings, a model derived from abundant surrogate labels may be deployed in a target population where gold-standard outcomes are unobserved.
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arXiv AI
Aug 6
Distributionally Robust Transfer Learning with Structurally Missing Covariates, with Application to Cross-National Cardiac Arrest Prediction
arXiv:2605. 24212v2 Announce Type: replace-cross Abstract: Deploying clinical prediction models across healthcare systems often fails when key training covariates are unavailable at deployment and labeled outcomes are limited in the target domain.
arXiv Machine Learning
Aug 4
Augmented Inverse Hybrid Weighting: Robust Inference under Deterministic and Random Distribution Shifts
arXiv:2608. 00701v1 Announce Type: cross Abstract: Reweighting source samples to match a target covariate distribution is a standard response to distribution shift when generalizing evidence from one population to another.
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 30
HERO: Improving the Reliability and Sensitivity of Generative Model Evaluation Using Historical Data
arXiv:2606. 29784v1 Announce Type: cross Abstract: Reliable generative AI models critically rely on expert human annotations to evaluate output quality, yet these "gold" labels are expensive to collect and limited in quantity.
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
Aug 4
Learning the Pareto Frontier of Predictive Models under Distribution Shift
arXiv:2608. 00632v1 Announce Type: new Abstract: Modern machine learning pipelines increasingly rely on reusing pretrained and foundation models across downstream tasks.
arXiv Machine Learning
Jun 18
When AUC Misleads: Polarization-Aware Evaluation of Deepfake Detectors under Domain Shift
arXiv:2606. 19184v1 Announce Type: cross Abstract: Recent advances in generative AI, such as diffusion models and face-swapping tools, have enabled the creation of highly realistic deepfakes, leading to real-world harms including financial fraud and non-consensual explicit content.
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 11
Point-Identification of a Robust Predictor Under Latent Shift with Imperfect Proxies
arXiv:2603. 15158v2 Announce Type: replace Abstract: Addressing the domain adaptation problem becomes more challenging when distribution shifts across domains stem from latent confounders that affect both covariates and outcomes.
arXiv Machine Learning
Jul 1
When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts
arXiv:2507. 14661v2 Announce Type: replace-cross Abstract: Semi-supervised domain adaptation (SSDA) seeks to achieve accurate predictions in a target domain with limited labeled target data by exploiting abundant source and unlabeled target data.
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.