3 citations · 7 across the 6 of their papers we have counts for
8 papers · 1 filter
Stability is Stable: Connections between Replicability, Privacy, and Adaptive Generalization
Mark Bun, Marco Gaboardi, Max Hopkins +5
The notion of replicable algorithms was introduced in Impagliazzo et al. [STOC '22] to describe randomized algorithms that are stable under the resampling of their inputs. More pre…
Do PAC-Learners Learn the Marginal Distribution?
Max Hopkins, Daniel M. Kane, Shachar Lovett +1
The Fundamental Theorem of PAC Learning asserts that learnability of a concept class is equivalent to the of empirical error in to its mean,…
Robust Empirical Risk Minimization with Tolerance
Robi Bhattacharjee, Max Hopkins, Akash Kumar +2
Developing simple, sample-efficient learning algorithms for robust classification is a pressing issue in today's tech-dominated world, and current theoretical techniques requiring…
Active Learning Polynomial Threshold Functions
Omri Ben-Eliezer, Max Hopkins, Chutong Yang +1
We initiate the study of active learning polynomial threshold functions (PTFs). While traditional lower bounds imply that even univariate quadratics cannot be non-trivially activel…
Realizable Learning is All You Need
Max Hopkins, Daniel M. Kane, Shachar Lovett +1
The equivalence of realizable and agnostic learnability is a fundamental phenomenon in learning theory. With variants ranging from classical settings like PAC learning and regressi…
Bounded Memory Active Learning through Enriched Queries
Max Hopkins, Daniel Kane, Shachar Lovett +1
The explosive growth of easily-accessible unlabeled data has lead to growing interest in active learning, a paradigm in which data-hungry learning algorithms adaptively select info…