arXiv Machine Learning
Sep 10

Large Classification-Risk-Optional Label Acquisition

arXiv:2609.06873v1 Announce Type: cross Abstract: We study how a limited labeling budget should be allocated to minimize multiclass zero-one classification risk. We consider parametric classification...

By F. Setoudehtanzangi, Geoffrey J. McLachlan
arXiv Machine Learning
2d ago

Learning Distributionally Robust First-Order Methods for Convex Optimization

The paper introduces a distributionally robust method for learning hyperparameters of first‑order convex optimization algorithms. By minimizing a Wasserstein‑robust performance estimation problem over a dataset of problem instances, the approach interpolates between classical learning‑to‑optimize (L2O) and worst‑case PEP design. The authors solve the resulting problem with stochastic gradient descent, provide high‑probability risk bounds, and demonstrate that the learned algorithms outperform both worst‑case optimal and vanilla L2O baselines on logistic regression, LASSO, and linear programming tasks.

By Vinit Ranjan, Jisun Park, Bartolomeo Stellato
arXiv Machine Learning
Sep 22

Classification with Abstention Under Class-Conditional Error Constraints

The paper investigates binary classification with abstention under separate class‑conditional error constraints, aiming to minimize abstention while keeping both error types below specified thresholds. It derives the distribution‑free minimax rate of excess abstention risk, introduces surrogate‑loss formulations for computational feasibility with models like neural networks, and provides finite‑sample guarantees for excess surrogate ambiguity risk. The authors also formulate the learning task as a constrained optimization problem, analyze its computational complexity in the convex setting, and empirically evaluate the approach against a competing method on several datasets.

By Mohammadreza M. Kalan, Yuyang Deng, Sanaz Hamidi