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).
By Sura Alhanouti, G\"uzin Bayraksan, Parinaz Naghizadeh
arXiv:2606. 10137v1 Announce Type: new Abstract: A common assumption in strategic classification is that the classifier is public knowledge.
By Ivri Hikri, Nir Rosenfeld
arXiv:2606. 01198v1 Announce Type: new Abstract: Strategic classification studies settings in which agents respond to a deployed classifier by modifying observable features at a cost.
By Siddharth Shrivastava, Mahvith Akshintala, B Vamsha Vardhan Reddy, Naresh Manwani, Sujit Gujar, Ganesh Ghalme
arXiv:2606. 19734v1 Announce Type: new Abstract: Federated bilevel optimization is widely used for nested learning problems across distributed clients, such as federated hyperparameter tuning and meta-learning under privacy and communication constraints.
By Liangxin Qian, Chang Liu, Xuanyu Cao, Jun Zhao, Kwok-Yan Lam
arXiv:2608. 15124v1 Announce Type: new Abstract: In contextual optimization, the decision-maker seeks optimal decisions to minimize a cost function, that varies based on observed features.
By Gar Goei Loke, Qinshen Tang, Yangge Xiao, Xun Zhang
The paper extends ex‑ante evaluation of Predict‑Then‑Optimize methods from binary to multiclass classification by simulating predictions at specified performance levels and mapping prediction errors to decision regret. It introduces a first‑order approximation that estimates regret from individual misclassifications, reducing computational effort. Experiments show the simulation accurately reproduces target performance and that the approximation is close for some problems, though it falters when simultaneous misclassifications interact significantly.
By Pieter Smet