arXiv:2606. 00202v1 Announce Type: cross Abstract: Standard machine learning pipelines often admit many near-optimal models.
Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.
arXiv:2608. 04310v1 Announce Type: new Abstract: The Rashomon effect describes the phenomenon that many models can achieve nearly equivalent performance on the same learning task, with significant ramifications for robustness, feature importance, and customizability.
arXiv:2511. 19636v2 Announce Type: replace-cross Abstract: In many machine learning problems, there may exist multiple models that achieve nearly identical predictive performance while relying on fundamentally different internal logic.
arXiv:2606. 30995v1 Announce Type: new Abstract: Recent work has shown that well-optimized individual decision trees can match complex black box models in some settings, primarily in noisy domains.
arXiv:2503. 12902v4 Announce Type: replace Abstract: Model trees provide an appealing way to perform interpretable machine learning for both classification and regression problems.
arXiv:2606. 19587v1 Announce Type: cross Abstract: We propose a scalable method for training prediction (machine learning) models in the predict-then-optimize paradigm, where model outputs serve as coefficients for a subsequent linear optimization task.
arXiv:2605. 30089v2 Announce Type: replace Abstract: Standard Set Representation Learning methods typically excel on curated data but often overlook the challenge of inference-time element corruption.