arXiv:2211. 14411v5 Announce Type: replace-cross Abstract: Hyperparameter optimization (HPO) is crucial for strong performance of deep learning algorithms and real-world applications often impose some constraints, such as on memory usage or latency, on top of the performance requirement.
By Shuhei Watanabe, Frank Hutter
arXiv:2606. 15569v1 Announce Type: new Abstract: Test-time training (TTT) adapts a pretrained model to each prompt via parameter updates, improving accuracy under pretraining-to-test distribution shifts.
By Tomoya Wakayama
arXiv:2602. 05786v3 Announce Type: replace Abstract: Tree-boosting is a widely used machine learning technique for tabular data.
By Floris Jan Koster, Fabio Sigrist
arXiv:2608. 13793v1 Announce Type: cross Abstract: Machine learning (ML) has become an indispensable part of modern engineering design workflows.
By Tyler R. Johnson, Kian Ben-Jacob, Christopher P. Muller, Ramin Bostanabad
arXiv:2412. 12807v4 Announce Type: replace-cross Abstract: Selective classification is a powerful tool for automated decision-making in high-risk scenarios, allowing classifiers to act only when confident and abstain when uncertainty is high.
By Mohamed Ndaoud, Peter Radchenko, Bradley Rava
arXiv:2405. 04376v4 Announce Type: replace Abstract: Hyperparameter tuning, particularly the selection of an appropriate learning rate in adaptive gradient training methods, remains a challenge.
By Yijiang Pang, Shuyang Yu, Bao Hoang, Jiayu Zhou