← Back to all news
arXiv Machine Learning By Jessica Finocchiaro, Sanket Shah, Milind Tambe

Contrasting Cost-Agnostic and Cost-Sensitive Losses under Limited Model Capacity via $\mathcal H$-consistency

Read the original on arXiv Machine Learning →

The paper investigates the difference between cost‑agnostic and cost‑sensitive loss functions when model capacity is limited. It shows that, unlike in ideal infinite‑capacity settings, optimizing a cost‑sensitive objective can yield a strictly better downstream decision than post‑processing a cost‑agnostic model. The authors prove this gap under a hypothesis class that can recover the optimal decision boundary but not the optimal cost‑agnostic hypothesis, and provide a simple example and empirical evidence on UCI datasets with simple models.

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.

Related stories

arXiv Statistics ML
1d ago

Constrained Classification and Policy Learning

The paper investigates the consistency of surrogate loss methods for classification and policy learning when the set of admissible classifiers is constrained, such as by interpretability or fairness requirements. It shows that hinge loss is the only surrogate that preserves consistency when constraints limit only the prediction set, but consistency can fail if constraints also restrict the functional form. The authors derive conditions guaranteeing consistency for hinge-risk-minimizing classifiers and use these results to design efficient hinge-loss-based procedures for monotone classification problems.

By Toru Kitagawa, Shosei Sakaguchi, Aleksey Tetenov
safety
More like this →
arXiv Machine Learning
Aug 4

T-TAMER: Provably Taming Trade-offs in ML Serving

arXiv:2509. 22992v2 Announce Type: replace Abstract: As machine learning models continue to grow in size and complexity, efficient serving faces increasingly broad trade-offs spanning accuracy, latency, resource usage, and other objectives.

By Yuanyuan Yang, Ruimin Zhang, Jamie Morgenstern, Haifeng Xu
nlpbenchmarks
More like this →
arXiv Machine Learning
Aug 28

Safety by Design: Realized-Cost Constraints for Contextual Bandits with Continuous Actions

The paper introduces a new approach to safety in contextual bandits with continuous actions, focusing on high‑probability constraints on the realized cost rather than expected cost. It presents the High‑Probability Constrained UCB algorithm, which balances reward exploration with conservative safety estimation, and provides theoretical regret guarantees for linear models and extensions to general function classes. Experiments demonstrate that this realized‑cost safety framework significantly reduces safety violations compared to expected‑cost constrained methods.

By Spyros Dragazis, Aldo Pacchiano
agentsreinforcement-learningroboticssafety
More like this →