arXiv Machine Learning August 5, 2026 By Vagner Santos, Victor Coscrato, Luben Cabezas, Rafael Izbicki, Thiago Ramos

LoBoost: Fast Model-Native Local Conformal Prediction for Gradient-Boosted Trees

Read the original on arXiv Machine Learning →

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

Hugging Face Trending Papers

Sep 21

Beyond Point Prediction: Artificial Representative Trees with Uncertainty

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...

benchmarks safety

arXiv Machine Learning

Sep 22

Beyond Point Prediction: Artificial Representative Trees with Uncertainty

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

benchmarks safety

arXiv Machine Learning

Aug 5

Double Descent in Gradient Boosting Decision Trees via Split-Candidate Scaling

arXiv:2608. 03111v1 Announce Type: new Abstract: Double descent is commonly studied by scaling an explicit capacity parameter, such as neural-network width.

By Ryuichi Kanoh

efficiency

Hugging Face Trending Papers

Jun 28

Self-Organized Conformal Prediction: Reducing Regional Coverage Gaps with Unsupervised Group Discovery

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.

rag benchmarks safety

arXiv Machine Learning

Jun 11

PCS-UQ: Uncertainty Quantification via the Predictability-Computability-Stability Framework

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

computer-vision benchmarks safety

arXiv AI

Aug 11

PATH: Next-Interval Prediction via Autoregressive Tree Hierarchy on Tabular Data

arXiv:2608. 08078v1 Announce Type: new Abstract: Interval prediction aims to achieve a target coverage level while producing intervals that are as short as possible.

By Pengxiang Cai, Wanchen Lian, Chenyang Liu, Xiaohan Li, Qingyuan Zeng, Jinhong Wang, Jintai Chen

One email a morning, machine-written

Related stories

Beyond Point Prediction: Artificial Representative Trees with Uncertainty

Beyond Point Prediction: Artificial Representative Trees with Uncertainty

Double Descent in Gradient Boosting Decision Trees via Split-Candidate Scaling

Self-Organized Conformal Prediction: Reducing Regional Coverage Gaps with Unsupervised Group Discovery

PCS-UQ: Uncertainty Quantification via the Predictability-Computability-Stability Framework

PATH: Next-Interval Prediction via Autoregressive Tree Hierarchy on Tabular Data