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Replicable Learning of Large-Margin Halfspaces
Feb. 22, 2024, 5:41 a.m. | Alkis Kalavasis, Amin Karbasi, Kasper Green Larsen, Grigoris Velegkas, Felix Zhou
cs.LG updates on arXiv.org arxiv.org
Abstract: We provide efficient replicable algorithms for the problem of learning large-margin halfspaces. Our results improve upon the algorithms provided by Impagliazzo, Lei, Pitassi, and Sorrell [STOC, 2022]. We design the first dimension-independent replicable algorithms for this task which runs in polynomial time, is proper, and has strictly improved sample complexity compared to the one achieved by Impagliazzo et al. [2022] with respect to all the relevant parameters. Moreover, our first algorithm has sample complexity that …
abstract algorithms arxiv complexity cs.lg design independent polynomial sample type
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