When Can Two Unsupervised Learners Achieve PAC Separation?
COLT '01/EuroCOLT '01 Proceedings of the 14th Annual Conference on Computational Learning Theory and and 5th European Conference on Computational Learning Theory
Learning a parallelepiped: cryptanalysis of GGH and NTRU signatures
EUROCRYPT'06 Proceedings of the 24th annual international conference on The Theory and Applications of Cryptographic Techniques
Effective principal component analysis
SISAP'12 Proceedings of the 5th international conference on Similarity Search and Applications
Learning mixtures of spherical gaussians: moment methods and spectral decompositions
Proceedings of the 4th conference on Innovations in Theoretical Computer Science
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We present a polynomial time algorithm to learn (in Valiant's PAC model) an arbitrarily oriented cube in n-space, given uniformly distributed sample points from it. In fact, we solve the more general problem of learning, in polynomial time, a linear (affine) transformation of a product distribution.