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
arXiv:2507. 05972v3 Announce Type: replace-cross Abstract: Pseudoentropy characterizations give quantitatively precise formulations of the relationship between computational hardness and computational randomness.
By Lunjia Hu, Salil Vadhan
arXiv:2607. 07085v1 Announce Type: cross Abstract: The Adaptive Data Analysis (ADA) problem formalizes the challenge of preventing false discovery and overfitting when a dataset is repeatedly reused.
By Edith Cohen, Haim Kaplan, Yishay Mansour, Shay Sapir, Uri Stemmer
arXiv:2607. 23361v1 Announce Type: cross Abstract: Language generation in the limit is an elegant model introduced by Kleinberg and Mullainathan [KM24] to formally study language generation by an algorithm that learns solely based on example strings.
By Debmalya Panigrahi, Fan Wei, Ian Zhang
arXiv:2609.23094v1 Announce Type: cross
Abstract: We study the number of prototypes needed to represent Boolean functions by nearest-neighbour classification. There are two distinct settings: the pro...
By Martin Anthony
arXiv:2607. 21761v1 Announce Type: cross Abstract: We prove function-theoretic analogues of a quantitative result of Hodges on extracting the order property from a sufficiently large 2-tree coded in a binary relation.
By G Conant, C Terry