A coupled-label bootstrap provides valid inference for OLS regressions that use AI/ML-generated binary labels despite misclassification errors, unlike standard fixed-label bootstraps.
In- ference for Regression with Variables Generated by AI or Machine Learning
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
verdicts
UNVERDICTED 3representative citing papers
A three-tower embedding model fine-tuned from Fashion CLIP combined with a latent-class deep demand system captures heterogeneous consumer aesthetics, price sensitivities, and substitution patterns from large-scale retail transaction data.
A new framework combines AI-derived concept embeddings with high-dimensional selective inference to enable statistically principled, interpretable discovery from unstructured data in empirical economics.
citing papers explorer
-
Bootstrapping with AI/ML-generated labels
A coupled-label bootstrap provides valid inference for OLS regressions that use AI/ML-generated binary labels despite misclassification errors, unlike standard fixed-label bootstraps.
-
A Deep Learning Approach to Heterogeneous Consumer Aesthetics in Fast Fashion
A three-tower embedding model fine-tuned from Fashion CLIP combined with a latent-class deep demand system captures heterogeneous consumer aesthetics, price sensitivities, and substitution patterns from large-scale retail transaction data.
-
Making Interpretable Discoveries from Unstructured Data: A High-Dimensional Multiple Hypothesis Testing Approach
A new framework combines AI-derived concept embeddings with high-dimensional selective inference to enable statistically principled, interpretable discovery from unstructured data in empirical economics.