Some of our results show cryptographic hardness of learning polynomial-size monotone circuits to accuracy only slightly greater than 1/2+1/√n; this accuracy ...
In this paper we establish cryptographic hardness results for learning various “simple” classes of monotone circuits, thus giving a computational analogue of ...
Abstract. A wide range of positive and negative results have been established for learning different classes of Boolean functions from uniformly distributed.
Motivated by this disparity between known positive results (for monotone functions) and negative results (for nonmonotone functions), we establish strong ...
Our main tool is a complexity-theoretic approach to hardness amplification via noise sensitivity of monotone functions that was pioneered by O'Donnell (JCSS~'04) ...
Dec 12, 2009 · In this paper we establish cryptographic hardness results for learning various “simple” classes of monotone circuits, thus giving a ...
Abstract: Over the years a range of positive algorithmic results have been obtained for learning various classes of monotone Boolean functions from ...
Our main tool is a complexity-theoretic approach to hardness amplification via noise sensitivity of monotone functions that was pioneered by O'Donnell (JCSS '04) ...
Optimal Cryptographic Hardness of Learning Monotone Functions by Dana Dachman-Soled, Homin K. Lee, Tal Malkin, Rocco A. Servedio, Andrew Wan, and Hoeteck Wee.
Bibliographic details on Optimal Cryptographic Hardness of Learning Monotone Functions.