Conformal prediction guarantees marginal coverage, but pooled calibration averages over heterogeneous regions and can mask regional undercoverage in safety-critical subgroups. We introduce Self-Organized Conformal Prediction (SOCP), a calibration scheme that discovers input-space groups with a Self-Organizing Map (SOM) and, at test time, draws a local calibration buffer from the query's best-matching unit (BMU) cell or a fixed grid neighborhood.
arXiv:2505. 08784v2 Announce Type: replace-cross Abstract: As machine learning (ML) enters high-stakes domains, trustworthy uncertainty quantification (UQ) is essential for safety.
By Abhineet Agarwal, Fange Xiao, Rebecca Barter, Omer Ronen, Boyu Fan, Bin Yu
arXiv:2608. 06206v1 Announce Type: cross Abstract: Conformal prediction endows arbitrary black-box predictors with finite-sample, distribution-free marginal coverage, yet marginal validity can hide severe covariate-specific miscalibration, while exact distribution-free conditional coverage is finite-sample unattainable.
By Anton Conrad, Rustam Isaev, Denis Belomestny, Eric Moulines, Sergey Samsonov
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:2608.23638v1 Announce Type: cross
Abstract: Two analysts who calibrate the same predictive model on independent samples will deploy different prediction sets every time, because the calibration...
By Marios Papamichalis, Regina Ruane, Theofanis Papamichalis
arXiv:2607. 18088v1 Announce Type: new Abstract: Standard evaluation of many recognition systems contains distribution shift by construction, since benchmarks place disjoint conditions in the training and test splits.
By Weijia Han, Lisha Qu
arXiv:2602. 22432v2 Announce Type: replace-cross Abstract: Gradient-boosted decision trees are among the strongest off-the-shelf predictors for tabular regression, but point predictions alone do not quantify uncertainty.
By Vagner Santos, Victor Coscrato, Luben Cabezas, Rafael Izbicki, Thiago Ramos
arXiv:2511.15146v2 Announce Type: replace
Abstract: Conformal prediction (CP) constructs uncertainty sets for model outputs with finite-sample coverage guarantees. Yet ranking scores is straightforwa...
By Eugene Ndiaye
arXiv:2604.22391v2 Announce Type: replace-cross
Abstract: The Super Learner (SL) is a widely used ensemble method that combines point predictions from a library of learners based on their predictive...
By Zhanli Wu, Fabrizio Leisen, Miguel-Angel Luque-Fernandez, F. Javier Rubio
arXiv:2608.23744v1 Announce Type: new
Abstract: Split conformal prediction, not the pruning rule, supplies finite-sample marginal coverage once a pruned model is fixed independently of the conformal...
By Ibne Farabi Shihab, Adria Binte Habib, Anuj Sharma
arXiv:2606. 28598v1 Announce Type: cross Abstract: Prediction sets should have high coverage to be useful, but some coverage notions are more practically relevant than others.
By Aabesh Bhattacharyya, Tiffany Ding, Rina Foygel Barber
The paper proposes an ensemble method for clusterwise regression that uses exact solutions on many small random subsamples. Each subsample is solved to global optimality, extended to the full data via nearest-surface assignment, and the resulting partitions are combined by voting or selection. The method achieves high accuracy even with up to 20% gross outliers and can estimate the trimming level without prior knowledge, outperforming traditional trimmed alternation in worst‑case scenarios.
By Samir Orujov