arXiv Machine Learning By Fangqin Zhou, Joaquin Vanschoren

Residual Correlation as a Diagnostic for Joint-Uncertainty Gains from GP Coregionalisation

Read the original on arXiv Machine Learning →

arXiv:2609. 30085v1 Announce Type: new Abstract: In multi-target regression, correlated targets are often coupled through multi-output Gaussian processes with an intrinsic model of coregionalisation (GP-ICM), assuming that sharing statistical strength improves overall performance.

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.

arXiv Machine Learning
Sep 24

CORE-STACK+: Meta-Learning for Deep Stacked Generalization

CORE-STACK+ is a new meta‑learning framework for deep stacked generalization that tackles two key problems in heterogeneous vision ensembles: prediction‑space multicollinearity and calibration collapse. It introduces a four‑step preconditioning pipeline—kernelized redundancy filtering, a lightweight differentiable meta‑feature gate, a spectrum‑adaptive ridge penalty, and a Laplace‑approximate Bayesian blender—to jointly improve conditioning and calibration. Across six vision benchmarks, CORE‑STACK+ boosts accuracy, reduces model count and inference cost, and significantly lowers expected calibration error compared to existing methods.

By Noor Islam S. Mohammad
arXiv Machine Learning
Sep 1

Prediction-Powered Conditional Inference

arXiv:2603.05575v2 Announce Type: replace-cross Abstract: We study prediction-powered conditional inference in the setting where labeled data are scarce, unlabeled covariates are abundant, and a blac...

By Yang Sui, Jin Zhou, Hua Zhou, Xiaowu Dai