arXiv AI
Jun 12
Valid Inference with Synthetic Data via Task Exchangeability
arXiv:2606. 13629v1 Announce Type: cross Abstract: There is a proliferation of work arguing for the use of synthetic data in scientific research.
General Synthetic-Powered Inference
arXiv:2509. 20345v3 Announce Type: replace-cross Abstract: The rapid proliferation of high-quality synthetic data -- generated by advanced AI models or collected as auxiliary data from related tasks -- presents both opportunities and challenges for statistical inference.
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arXiv Machine Learning
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Conformal Prediction for Molecular Properties under Label Shift
arXiv:2608. 17678v1 Announce Type: new Abstract: Drug discovery and development underpins healthcare but remains costly and failure-prone.
arXiv Machine Learning
Jun 8
Synthics: Synthetic Physics-like Datasets for Machine Learning
arXiv:2606. 06724v1 Announce Type: new Abstract: Representative data is fundamental in machine learning, as limited data hinders generalisation.
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 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.
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.
arXiv Machine Learning
Jun 30
Inference-time optimization for experiment-grounded protein ensemble generation
arXiv:2602. 24007v3 Announce Type: replace-cross Abstract: Protein function relies on dynamic conformational ensembles, yet current generative models like AlphaFold3 often fail to produce ensembles that match experimental data.
arXiv Machine Learning
Jul 13
SYNRARE: Synthetic Rare Disease EHR Generation for ML Benchmarking
arXiv:2607. 09404v1 Announce Type: new Abstract: Motivation: Rare disease (RD) diagnosis is frequently delayed due to the similarities in symptoms to common disease variants.
arXiv Machine Learning
Jun 17
Recursive Learning Without Collapse: A Weighting-Based Stabilization Framework
arXiv:2502. 18049v5 Announce Type: replace-cross Abstract: Recent studies identified an intriguing phenomenon in recursive generative model training known as model collapse, where models trained on data generated by previous models exhibit severe performance degradation.
arXiv AI
Jun 26
Limited Reference, Reliable Generation: A Two-Component Framework for Tabular Data Generation in Low-Data Regimes
arXiv:2509. 09960v2 Announce Type: replace-cross Abstract: Synthetic tabular data generation is increasingly essential in machine learning, supporting downstream applications when real-world, high-quality tabular data is insufficient.
arXiv Machine Learning
Jul 31
How Can We Synthesize High-Quality Pretraining Data? A Systematic Study of Prompt Design, Generator Model, and Source Data
arXiv:2604. 13977v2 Announce Type: replace-cross Abstract: Synthetic data is a standard component in training large language models, yet systematic comparisons across design dimensions, including rephrasing strategy, generator model, and source data, remain absent.
arXiv AI
Jun 4
Generative Augmented Inference
arXiv:2604. 14575v2 Announce Type: replace-cross Abstract: Large language models enable inexpensive AI-generated annotations, but using them reliably for causal inference remains challenging.