### Abstract

We study the learnability of sums of independent integer random variables given a bound on the size of the union of their supports. For A ⊂ ℤ
_{+}
, a sum of independent random variables with collective support A (called an A-sum in this paper) is a distribution S = X
_{1}
+ • • • + X
_{N}
where the Xi's are mutually independent (but not necessarily identically distributed) integer random variables with ∪isupp(Xi) ⊂ A. We give two main algorithmic results for learning such distributions: 1) For the case |A| = 3, we give an algorithm for learning A-sums to accuracy ϵ that uses poly(1/ϵ) samples and runs in time poly(1/ϵ), independent of N and of the elements of A. 2) For an arbitrary constant k ≥ 4, if A = {a
_{1}
,⋯, a
_{k}
} with 0 < a
_{1}
<⋯ < a
_{k}
, we give an algorithm that uses poly(1/ϵ) • log log a
_{k}
samples (independent of N) and runs in time poly(1/ϵ, loga
_{k}
). We prove an essentially matching lower bound: if |A| = 4, then any algorithm must use Ω(log log a
_{4}
) samples even for learning to constant accuracy. We also give similar-in-spirit (but quantitatively very different) algorithmic results, and essentially matching lower bounds, for the case in which A is not known to the learner. Our learning algorithms employ new limit theorems which may be of independent interest. Our lower bounds rely on equidistribution type results from number theory. Our algorithms and lower bounds together settle the question of how the sample complexity of learning sums of independent integer random variables scales with the elements in the union of their supports, both in the known-support and unknown-support settings. Finally, all our algorithms easily extend to the "semi-agnostic" learning model, in which training data is generated from a distribution that is only cϵ-close to some A-sum for a constant c > 0.

Original language | English (US) |
---|---|

Title of host publication | Proceedings - 59th Annual IEEE Symposium on Foundations of Computer Science, FOCS 2018 |

Editors | Mikkel Thorup |

Publisher | IEEE Computer Society |

Pages | 297-308 |

Number of pages | 12 |

ISBN (Electronic) | 9781538642306 |

DOIs | |

State | Published - Nov 30 2018 |

Event | 59th Annual IEEE Symposium on Foundations of Computer Science, FOCS 2018 - Paris, France Duration: Oct 7 2018 → Oct 9 2018 |

### Publication series

Name | Proceedings - Annual IEEE Symposium on Foundations of Computer Science, FOCS |
---|---|

Volume | 2018-October |

ISSN (Print) | 0272-5428 |

### Other

Other | 59th Annual IEEE Symposium on Foundations of Computer Science, FOCS 2018 |
---|---|

Country | France |

City | Paris |

Period | 10/7/18 → 10/9/18 |

### Fingerprint

### Keywords

- Central limit theorems
- Distribution learning
- Sample complexity
- Sums of independent random variables
- Unsupervised learning

### ASJC Scopus subject areas

- Computer Science(all)

### Cite this

*Proceedings - 59th Annual IEEE Symposium on Foundations of Computer Science, FOCS 2018*(pp. 297-308). [8555114] (Proceedings - Annual IEEE Symposium on Foundations of Computer Science, FOCS; Vol. 2018-October). IEEE Computer Society. https://doi.org/10.1109/FOCS.2018.00036

}

*Proceedings - 59th Annual IEEE Symposium on Foundations of Computer Science, FOCS 2018.*, 8555114, Proceedings - Annual IEEE Symposium on Foundations of Computer Science, FOCS, vol. 2018-October, IEEE Computer Society, pp. 297-308, 59th Annual IEEE Symposium on Foundations of Computer Science, FOCS 2018, Paris, France, 10/7/18. https://doi.org/10.1109/FOCS.2018.00036

**Learning sums of independent random variables with sparse collective support.** / De, Anindya; Long, Philip M.; Servedio, Rocco A.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Learning sums of independent random variables with sparse collective support

AU - De, Anindya

AU - Long, Philip M.

AU - Servedio, Rocco A.

PY - 2018/11/30

Y1 - 2018/11/30

N2 - We study the learnability of sums of independent integer random variables given a bound on the size of the union of their supports. For A ⊂ ℤ + , a sum of independent random variables with collective support A (called an A-sum in this paper) is a distribution S = X 1 + • • • + X N where the Xi's are mutually independent (but not necessarily identically distributed) integer random variables with ∪isupp(Xi) ⊂ A. We give two main algorithmic results for learning such distributions: 1) For the case |A| = 3, we give an algorithm for learning A-sums to accuracy ϵ that uses poly(1/ϵ) samples and runs in time poly(1/ϵ), independent of N and of the elements of A. 2) For an arbitrary constant k ≥ 4, if A = {a 1 ,⋯, a k } with 0 < a 1 <⋯ < a k , we give an algorithm that uses poly(1/ϵ) • log log a k samples (independent of N) and runs in time poly(1/ϵ, loga k ). We prove an essentially matching lower bound: if |A| = 4, then any algorithm must use Ω(log log a 4 ) samples even for learning to constant accuracy. We also give similar-in-spirit (but quantitatively very different) algorithmic results, and essentially matching lower bounds, for the case in which A is not known to the learner. Our learning algorithms employ new limit theorems which may be of independent interest. Our lower bounds rely on equidistribution type results from number theory. Our algorithms and lower bounds together settle the question of how the sample complexity of learning sums of independent integer random variables scales with the elements in the union of their supports, both in the known-support and unknown-support settings. Finally, all our algorithms easily extend to the "semi-agnostic" learning model, in which training data is generated from a distribution that is only cϵ-close to some A-sum for a constant c > 0.

AB - We study the learnability of sums of independent integer random variables given a bound on the size of the union of their supports. For A ⊂ ℤ + , a sum of independent random variables with collective support A (called an A-sum in this paper) is a distribution S = X 1 + • • • + X N where the Xi's are mutually independent (but not necessarily identically distributed) integer random variables with ∪isupp(Xi) ⊂ A. We give two main algorithmic results for learning such distributions: 1) For the case |A| = 3, we give an algorithm for learning A-sums to accuracy ϵ that uses poly(1/ϵ) samples and runs in time poly(1/ϵ), independent of N and of the elements of A. 2) For an arbitrary constant k ≥ 4, if A = {a 1 ,⋯, a k } with 0 < a 1 <⋯ < a k , we give an algorithm that uses poly(1/ϵ) • log log a k samples (independent of N) and runs in time poly(1/ϵ, loga k ). We prove an essentially matching lower bound: if |A| = 4, then any algorithm must use Ω(log log a 4 ) samples even for learning to constant accuracy. We also give similar-in-spirit (but quantitatively very different) algorithmic results, and essentially matching lower bounds, for the case in which A is not known to the learner. Our learning algorithms employ new limit theorems which may be of independent interest. Our lower bounds rely on equidistribution type results from number theory. Our algorithms and lower bounds together settle the question of how the sample complexity of learning sums of independent integer random variables scales with the elements in the union of their supports, both in the known-support and unknown-support settings. Finally, all our algorithms easily extend to the "semi-agnostic" learning model, in which training data is generated from a distribution that is only cϵ-close to some A-sum for a constant c > 0.

KW - Central limit theorems

KW - Distribution learning

KW - Sample complexity

KW - Sums of independent random variables

KW - Unsupervised learning

UR - http://www.scopus.com/inward/record.url?scp=85059818867&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85059818867&partnerID=8YFLogxK

U2 - 10.1109/FOCS.2018.00036

DO - 10.1109/FOCS.2018.00036

M3 - Conference contribution

T3 - Proceedings - Annual IEEE Symposium on Foundations of Computer Science, FOCS

SP - 297

EP - 308

BT - Proceedings - 59th Annual IEEE Symposium on Foundations of Computer Science, FOCS 2018

A2 - Thorup, Mikkel

PB - IEEE Computer Society

ER -