arXiv:2608. 06337v1 Announce Type: cross Abstract: A monotone adversary observes an i.
By Anay Mehrotra
arXiv:2602. 06257v2 Announce Type: replace Abstract: Online strategic classification studies settings in which agents strategically modify their features to obtain favorable predictions.
By Chase Hutton, Adam Melrod, Han Shao
arXiv:2608. 13514v1 Announce Type: cross Abstract: We revisit the problem of learning predictors robust to adversarial examples at test-time.
By Omar Montasser
We revisit the problem of learning predictors robust to adversarial examples at test-time. We prove that VC classes are adversarially robustly learnable with sample complexity linear in the VC dimension $d$, providing an exponential improvement over the previous upper bound of Montasser, Hanneke, and Srebro (2019).
arXiv:2608. 01320v1 Announce Type: cross Abstract: Language generation in the limit is a theoretical framework for studying how a generator can learn to produce new valid strings from a stream of positive examples.
By Ziyi Cai, Shuangping Li, Yiheng Shen, Kangning Wang, Peng Zhang
The paper investigates learning with monotone adversarial corruptions, extending previous binary classification results to multiclass and partial binary settings. It shows that even a small number of strategically inserted corrupted examples can render a learnable multiclass problem with DS dimension 2 completely unlearnable, and provides matching upper bounds when the adversary’s budget is sublinear. The work also demonstrates that classic error rates remain attainable under bounded or limited‑view adversaries.
By Julian Asilis, Shaddin Dughmi, Chirag Pabbaraju