Parity learning

Known as: Learning Parity with Noise 
Parity learning is a problem in machine learning. An algorithm that solves this problem must guess the function ƒ, given some samples (x, ƒ(x)) and… (More)
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Topic mentions per year

Topic mentions per year

2000-2017
05101520002017

Papers overview

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2016
2016
  • Ran Raz
  • 2016 IEEE 57th Annual Symposium on Foundations of…
  • 2016
We prove that any algorithm for learning parities requires either a memory of quadratic size or an exponential number of samples… (More)
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2012
2012
We construct a perfectly binding string commitment scheme whose security is based on the learning parity with noise (LPN… (More)
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2012
2012
Given a set of n d-dimensional Boolean vectors with the promise that the vectors are chosen uniformly at random with the… (More)
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Review
2012
Review
2012
The Learning Parity with Noise (LPN) problem has recently found many applications in cryptography as the hardness assumption… (More)
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Highly Cited
2011
Highly Cited
2011
We construct efficient authentication protocols and message authentication codes (MACs) whose security can be reduced to the… (More)
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2009
2009
We study the learnability of several fundamental concept classes in the agnostic learning framework of Haussler [Hau92] and… (More)
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2008
2008
The motivating problem is agnostically learning parity functions, i.e., parity with arbitrary or adversarial noise. Specifically… (More)
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Highly Cited
2006
Highly Cited
2006
We address well-studied problems concerning the learn-ability of parities and halfspaces in the presence of classification noise… (More)
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Highly Cited
2005
Highly Cited
2005
In this paper, we study the problem of learning phylogenies and hidden Markov models. We call a Markov model nonsingular if all… (More)
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Highly Cited
2005
Highly Cited
2005
Forgery and counterfeiting are emerging as serious security risks in low-cost pervasive computing devices. These devices lack the… (More)
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