Corpus ID: 7860945

# Sample complexity of population recovery

@article{Polyanskiy2017SampleCO,
title={Sample complexity of population recovery},
author={Yury Polyanskiy and Ananda Theertha Suresh and Yihong Wu},
journal={ArXiv},
year={2017},
volume={abs/1702.05574}
}
• Published 2017
• Mathematics, Computer Science
• ArXiv
• The problem of population recovery refers to estimating a distribution based on incomplete or corrupted samples. Consider a random poll of sample size $n$ conducted on a population of individuals, where each pollee is asked to answer $d$ binary questions. We consider one of the two polling impediments: (a) in lossy population recovery, a pollee may skip each question with probability $\epsilon$; (b) in noisy population recovery, a pollee may lie on each question with probability \$\epsilon… CONTINUE READING

#### Citations

##### Publications citing this paper.
SHOWING 1-10 OF 10 CITATIONS

## Extrapolating the profile of a finite population

• Mathematics, Computer Science
• COLT
• 2020
VIEW 2 EXCERPTS
CITES BACKGROUND

## Efficient average-case population recovery in the presence of insertions and deletions

• Mathematics, Biology, Computer Science
• APPROX-RANDOM
• 2019

## Beyond Trace Reconstruction: Population Recovery from the Deletion Channel

• Mathematics, Computer Science
• 2019 IEEE 60th Annual Symposium on Foundations of Computer Science (FOCS)
• 2019

## Estimating the Number of Connected Components in a Graph via Subgraph Sampling

• Mathematics, Computer Science
• ArXiv
• 2018

## Sharp bounds for population recovery

• Mathematics, Computer Science
• ArXiv
• 2017
VIEW 9 EXCERPTS
CITES RESULTS & METHODS
HIGHLY INFLUENCED

## Statistical Windows in Testing for the Initial Distribution of a Reversible Markov Chain

• Mathematics, Computer Science
• AISTATS
• 2019
VIEW 1 EXCERPT
CITES BACKGROUND

## Dualizing Le Cam's method, with applications to estimating the unseens

• Mathematics, Computer Science
• ArXiv
• 2019

## Bias-Aware Confidence Intervals for Empirical Bayes Analysis

• Mathematics
• 2019
VIEW 1 EXCERPT
CITES BACKGROUND

#### References

##### Publications referenced by this paper.
SHOWING 1-10 OF 33 REFERENCES

## Population recovery and partial identification

• Mathematics, Computer Science
• 2012 IEEE 53rd Annual Symposium on Foundations of Computer Science
• 2012
VIEW 3 EXCERPTS

## Noisy Population Recovery in Polynomial Time

• Computer Science, Mathematics
• 2016 IEEE 57th Annual Symposium on Foundations of Computer Science (FOCS)
• 2016

## A Polynomial Time Algorithm for Lossy Population Recovery

• Computer Science, Mathematics
• 2013 IEEE 54th Annual Symposium on Foundations of Computer Science
• 2013
VIEW 7 EXCERPTS
HIGHLY INFLUENTIAL

## Improved Noisy Population Recovery, and Reverse Bonami-Beckner Inequality for Sparse Functions

• Mathematics, Computer Science
• STOC '15
• 2015
VIEW 3 EXCERPTS

## Finding Heavy Hitters from Lossy or Noisy Data

• Computer Science, Mathematics
• APPROX-RANDOM
• 2013
VIEW 6 EXCERPTS
HIGHLY INFLUENTIAL

## On the learnability of discrete distributions

VIEW 3 EXCERPTS

## Learning Arbitrary Statistical Mixtures of Discrete Distributions

• Mathematics, Computer Science
• STOC '15
• 2015
VIEW 1 EXCERPT

## Optimal mean-based algorithms for trace reconstruction

• Mathematics, Computer Science
• STOC
• 2017
VIEW 10 EXCERPTS
HIGHLY INFLUENTIAL

## Sharp bounds for population recovery

• Mathematics, Computer Science
• ArXiv
• 2017
VIEW 2 EXCERPTS

## Learning mixtures of product distributions over discrete domains

• Mathematics, Computer Science
• 46th Annual IEEE Symposium on Foundations of Computer Science (FOCS'05)
• 2005
VIEW 3 EXCERPTS