An asymmetric variant of minimally consistent concept classes is introduced and used to provide an exact characterization of proper learning with improvements in the real-izable setting, and positive results for more natural Euclidean ball improvement sets are given.
A noisy expert model is proposed to explain why OPD can outperform SFT when training language models from imperfect teachers, and it is proved that online interaction with the noisy expert via a novel variant of OPD enables polynomial dependence on the horizon in general.
V. Sriraman, Peihan Liu, Daniel Hsu et al.· arXiv.org· 0 citations
When a classifier is trained by actively choosing which examples to label, should its labeling budget depend on which notion of fairness it must satisfy? We answer this with the first information-theoretic lower bounds for group-fair active learning, and the answer is a sharp hierarchy across the three standard notions...
Chang Lu, Yizheng Zhao· Proceedings of the 32nd ACM...· 0 citations
A decision-aware weak-to-strong (W2S) framework that leverages both labeled and unlabeled data to improve contextual stochastic optimization and establishes a non-asymptotic upper bound on the excess decision risk of W2S and a complementary lower bound for a strong-only benchmark.
This work examines intrinsic limits of data-driven decision systems from an information-theoretic and interaction-based perspective and describes decision systems, including LLM-integrated agent architectures, as feedback-driven stochastic processes where state-dependent dynamics may induce emergent macroscopic behavio...
This thesis is a distinction that is easy to miss: detecting that such a signal helps on average is not the same as learning to act on it per instance, and a reward-SNR floor governs when the second is even possible, and a reward-SNR detectability floor is explained.
A novel perturbation test based on a nonsmooth max-difference revenue statistic comparing the best null assortment with the best alternative assortment and asymptotic validity of the proposed p-value under adaptive assortment selection is proposed.
Pengyu Li, Shuting Shen· 0 citations
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