arXiv Statistics ML

Cluster-Robust Prediction-Powered Inference

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.

By Kianoosh Ashouritaklimi, Valentin Kilian, Daolang Huang, Tom Rainforth, Fran\c{c}ois Caron
arXiv Machine Learning
Aug 11

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.

By Yilin Song, Dan M. Kluger, Harsh Parikh, Tian Gu
arXiv Machine Learning
Oct 7

Performative Prediction with Selective Labels

arXiv:2610.08272v1 Announce Type: new Abstract: Many social applications of machine learning exhibit performative effects: population behavior changes in response to deployed models. Performative pre...

By Giovani Valdrighi, Isabel Valera, Marcos Medeiros Raimundo
arXiv AI
Jun 6

Correcting Prompt Dependence in LLM Benchmarks: A Bayesian Hierarchical Model with Embedding-Space Clustering

arXiv:2510. 05709v2 Announce Type: replace-cross Abstract: LLM benchmarking metrics often misstate performance and uncertainty as they rely on two assumptions that frequently do not hold in practice: (i) a sufficient number of evaluations are available for classical inference, and (ii) test prompts are independent.

By Mary Llewellyn, Isobel Thornton, James Bishop, Annie Gray
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
5d 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
arXiv Machine Learning
Sep 2

SAGE: Subpopulation-Aware Generative Enhancement for Mitigating Spurious Correlations

SAGE (Subpopulation-Aware Generative Enhancement) is a two-stage generative augmentation framework designed to mitigate spurious correlations in machine learning when group labels are unavailable. It uses cluster-derived sub-labels and class labels to fine‑tune a conditional generative model and text encoder, producing synthetic data that fills underrepresented regions and creates a balanced validation set for last‑layer reweighting. Experiments show SAGE improves worst‑group accuracy to 89.5%, 85.7%, and 79.1% on Waterbirds, CelebA, and MetaShift, outperforming existing group‑label‑free baselines by up to 7.7 percentage points.

By Yiming Luo, Rongqiang Zhao, Jie Liu