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Electronic Colloquium on Computational Complexity

Under the auspices of the Computational Complexity Foundation (CCF)

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Reports tagged with computational learning:
TR96-009 | 17th January 1996
Francesco Bergadano, Nader Bshouty, Christino Tamon, Stefano Varricchio

On Learning Branching Programs and Small Depth Circuits

This paper studies the learnability of branching programs and small depth
circuits with modular and threshold gates in both the exact and PAC learning
models with and without membership queries. Some of the results extend earlier
works in [GG95,ERR95,BTW95]. The main results are as follows. For
branching programs we ... more >>>

TR06-059 | 3rd May 2006
Vitaly Feldman, Parikshit Gopalan, Subhash Khot, Ashok Kumar Ponnuswami

New Results for Learning Noisy Parities and Halfspaces

We address well-studied problems concerning the learnability of parities and halfspaces in the presence of classification noise.

Learning of parities under the uniform distribution with random classification noise,also called the noisy parity problem is a famous open problem in computational learning. We reduce a number of basic problems regarding ... more >>>

TR07-073 | 3rd August 2007
Parikshit Gopalan, Subhash Khot, Rishi Saket

Hardness of Reconstructing Multivariate Polynomials over Finite Fields

We study the polynomial reconstruction problem for low-degree
multivariate polynomials over finite fields. In the GF[2] version of this problem, we are given a set of points on the hypercube and target values $f(x)$ for each of these points, with the promise that there is a polynomial over GF[2] of ... more >>>

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