13 citations · 14 across the 19 of their papers we have counts for
5 papers · 1 filter
Testable Learning with Distribution Shift
Adam R. Klivans, Konstantinos Stavropoulos, Arsen Vasilyan
We revisit the fundamental problem of learning with distribution shift, in which a learner is given labeled samples from training distribution , unlabeled samples from test dist…
Local Lipschitz Filters for Bounded-Range Functions with Applications to Arbitrary Real-Valued Functions
Jane Lange, Ephraim Linder, Sofya Raskhodnikova +1
We study local filters for the Lipschitz property of real-valued functions , where the Lipschitz property is defined with respect to an arbitrary undirected graph $…
Agnostic proper learning of monotone functions: beyond the black-box correction barrier
Jane Lange, Arsen Vasilyan
We give the first agnostic, efficient, proper learning algorithm for monotone Boolean functions. Given uniformly random examples of an unknown…
Tester-Learners for Halfspaces: Universal Algorithms
Aravind Gollakota, Adam R. Klivans, Konstantinos Stavropoulos +1
We give the first tester-learner for halfspaces that succeeds universally over a wide class of structured distributions. Our universal tester-learner runs in fully polynomial time…
An Efficient Tester-Learner for Halfspaces
Aravind Gollakota, Adam R. Klivans, Konstantinos Stavropoulos +1
We give the first efficient algorithm for learning halfspaces in the testable learning model recently defined by Rubinfeld and Vasilyan (2023). In this model, a learner certifies t…