Random forests (RFs) predict well but are opaque, whereas single decision trees are interpretable but unstable. Artificial representative trees (ARTs) were developed as interpretable surrogate models...
arXiv:2603.23923v2 Announce Type: replace-cross
Abstract: Predictive inference is a fundamental task in statistics, traditionally addressed using parametric assumptions about the data distribution an...
By Matteo Sesia, Stefano Favaro
arXiv:2609.24528v1 Announce Type: cross
Abstract: Random forests (RFs) predict well but are opaque, whereas single decision trees are interpretable but unstable. Artificial representative trees (ARTs...
By Lea L. Mairh\"ofer, Silke Szymczak, Bj\"orn-Hergen Laabs, Tuwe L\"ofstr\"om-Cavallin
arXiv:2602. 14913v2 Announce Type: replace Abstract: Conformal prediction (CP) offers distribution-free marginal coverage guarantees under an exchangeability assumption, but these guarantees can fail if the data distribution shifts.
By Farbod Siahkali, Ashwin Verma, Vijay Gupta
arXiv:2609.17091v1 Announce Type: new
Abstract: Multi-target regression requires a model to simultaneously predict several related outputs. Conformal prediction provides distribution-free, finite-sam...
By Sylvain Rousseau (Heudiasyc), Soundouss Messoudi (Heudiasyc)
arXiv:2606. 31600v1 Announce Type: cross Abstract: Conformal prediction and its variants, including the split conformal prediction, provide a distribution-free framework for uncertainty quantification by constructing prediction intervals or sets with finite-sample coverage guarantees.
By Sayan Das, Bahram Yaghooti, Todd A. Kuffner, Soumendra N. Lahiri