## Abstract

We consider the problem of learning k-parities in the online mistake-bound model: given a hidden vector x∈{0,1}^{n} where the hamming weight of x is k and a sequence of “questions” a_{1},a_{2},…∈{0,1}^{n}, where the algorithm must reply to each question with 〈a_{i},x〉(mod2), what is the best trade-off between the number of mistakes made by the algorithm and its time complexity? We improve the previous best result of Buhrman et al. [3] by an exp(k) factor in the time complexity. Next, we consider the problem of learning k-parities in the PAC model in the presence of random classification noise of rate [Formula Presented]. Here, we observe that even in the presence of classification noise of non-trivial rate, it is possible to learn k-parities in time better than (nk/2), whereas the current best algorithm for learning noisy k-parities, due to Grigorescu et al. [9], inherently requires time (nk/2) even when the noise rate is polynomially small.

Original language | English |
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Pages (from-to) | 249-256 |

Number of pages | 8 |

Journal | Theoretical Computer Science |

Volume | 840 |

DOIs | |

Publication status | Published - 6 Nov 2020 |

MoE publication type | A1 Journal article-refereed |

## Keywords

- Learning k parities
- Learning sparse parities
- Learning sparse parities with noise
- Mistake bound model
- PAC model