arXiv:2607. 23388v1 Announce Type: cross Abstract: As constrained learning becomes increasingly common, models are trained under explicit feasibility requirements to enforce fairness, safety, robustness, regulariza- tion, and physics or logic constraints.
By Xin Wang (Jeff), R. Tyrrell Rockafellar (Jeff), Xuegang (Jeff), Ban
The paper introduces a control‑variable framework for deep neural networks to mitigate omitted variable bias, particularly shortcut learning where covariates like demographics influence predictions. It refits the final layer of a pre‑trained network using cross‑fitting with ridge penalisation, orthogonalises covariate effects, and marginalises predictions over covariate distributions to achieve unbiased, interpretable results. Experiments on simulated images and neuroimaging data show consistent estimation of true effects and performance close to models trained on unconfounded data.
By Manuel Pfeuffer, Roshan Prakash Rane, Kerstin Ritter, Sonja Greven
arXiv:2606. 18307v1 Announce Type: cross Abstract: Optimizing the training data distribution for Supervised Fine-Tuning (SFT) dictates the capability of Large Language Models (LLMs).
By Zefan Wang, Lincheng Li, Tianyu Yu, Yuan Yao
arXiv:2606. 05247v1 Announce Type: new Abstract: Enforcing nonlinear inequality constraints in neural networks remains challenging, especially when the output is subject to many coupled constraints.
By Ziqian Wang, Chenxi Fang, Zhen Zhang
The paper argues against using fixed weighted-sum penalties to enforce constraints in deep learning, citing three main issues: non-equivalence of penalized and constrained problems in non-convex settings, difficulty in selecting appropriate penalty coefficients, and the softening of hard requirements. It contends that these problems can lead to non-compliance and relaxed constraints. The authors recommend directly solving the constrained problem when non-negotiable requirements are present.
By Juan Ramirez, Meraj Hashemizadeh, Simon Lacoste-Julien
arXiv:2606. 13803v1 Announce Type: new Abstract: Enforcing functional inequality constraints such as monotonicity and convexity in neural networks is a fundamental challenge in many industrial and scientific applications.
By Ruben Wiedemann, Antoine Jacquier, Lukas Gonon