新研究通过依赖决策的成本和非线性模型解决战略分类问题 · 跟踪4个来源

报道来源 [4]

1. arXiv cs.LG TIER_1 English(EN) · Sura Alhanouti, G\"uzin Bayraksan, Parinaz Naghizadeh · 2026-06-30 04:00

   Robust Strategic Classification under Decision-Dependent Cost Uncertainty

   arXiv:2606.30136v1 Announce Type: new Abstract: Humans facing algorithmic decision systems have been found to ``game'' them by altering their input data (at a cost to them) in order to favorably change the algorithmic outcomes they receive (at a cost to the algorithm). The growin…

2. arXiv cs.LG TIER_1 English(EN) · Parinaz Naghizadeh · 2026-06-29 11:15

   Robust Strategic Classification under Decision-Dependent Cost Uncertainty

   Humans facing algorithmic decision systems have been found to ``game'' them by altering their input data (at a cost to them) in order to favorably change the algorithmic outcomes they receive (at a cost to the algorithm). The growing literature on strategic classification seeks t…

3. arXiv cs.LG TIER_1 English(EN) · Jack Geary, Boyan Gao, Henry Gouk · 2026-06-29 04:00

   Non-Linear Strategic Classification Made Practical

   arXiv:2606.28204v1 Announce Type: cross Abstract: Algorithmic developments in Strategic Classification have been mostly limited to linear classifiers in settings where the best response has a closed-form solution or can be easily approximated. While some work has explored the rol…

4. arXiv cs.LG TIER_1 English(EN) · Henry Gouk · 2026-06-26 15:52

   非线性战略分类的实用化

   Algorithmic developments in Strategic Classification have been mostly limited to linear classifiers in settings where the best response has a closed-form solution or can be easily approximated. While some work has explored the role of non-linear classifiers in strategic settings,…