| Texto completo | |
| Autor(es): |
Cabezas, Luben M. C.
;
Santos, Vagner S.
;
Ramos, Thiago R.
;
Izbicki, Rafael
Número total de Autores: 4
|
| Tipo de documento: | Artigo Científico |
| Fonte: | NEURIPS WORKSHOPS, 2020; v. 286, p. 28-pg., 2025-01-01. |
| Resumo | |
Conformal prediction methods create prediction bands with distribution-free guarantees but do not explicitly capture epistemic uncertainty, which can lead to overconfident predictions in data-sparse regions. Although recent conformal scores have been developed to address this limitation, they are typically designed for specific tasks, such as regression or quantile regression. Moreover, they rely on particular modeling choices for epistemic uncertainty, restricting their applicability. We introduce EPICSCORE, a model-agnostic approach that enhances any conformal score by explicitly integrating epistemic uncertainty. Leveraging Bayesian techniques such as Gaussian Processes, Monte Carlo Dropout, or Bayesian Additive Regression Trees, EPICSCORE adaptively expands predictive intervals in regions with limited data while maintaining compact intervals where data is abundant. As with any conformal method, it preserves finite-sample marginal coverage. Additionally, it also achieves asymptotic conditional coverage. Experiments demonstrate its good performance compared to existing methods. Designed for compatibility with any Bayesian model, but equipped with distribution-free guarantees, EPICSCORE provides a general-purpose framework for uncertainty quantification in prediction problems. (AU) | |
| Processo FAPESP: | 23/07068-1 - Aprendizado estatístico de máquina - em direção a uma melhor quantificação de incerteza |
| Beneficiário: | Rafael Izbicki |
| Modalidade de apoio: | Auxílio à Pesquisa - Regular |
| Processo FAPESP: | 22/08579-7 - Validação e calibração de modelos preditivos |
| Beneficiário: | Luben Miguel Cruz Cabezas |
| Modalidade de apoio: | Bolsas no Brasil - Doutorado Direto |
| Processo FAPESP: | 19/11321-9 - Redes neurais em problemas de inferência estatística |
| Beneficiário: | Rafael Izbicki |
| Modalidade de apoio: | Auxílio à Pesquisa - Regular |
| Processo FAPESP: | 23/05587-1 - Estimação de densidades condicionais por meio de ondaletas usando o método FlexCode |
| Beneficiário: | Vagner Silva Santos |
| Modalidade de apoio: | Bolsas no Brasil - Iniciação Científica |