# Preserving Randomness for Adaptive Algorithms

By William M. Hoza and Adam R. Klivans

Read the paper: arXiv • ECCC • RANDOM proceedings

## Abstract (for specialists)

Suppose \(\mathsf{Est}\) is a randomized estimation algorithm that uses \(n\) random bits and outputs values in \(\mathbb{R}^d\). We show how to execute \(\mathsf{Est}\) on \(k\) adaptively chosen inputs using only \(n + O(k \log(d + 1))\) random bits instead of the trivial \(nk\) (at the cost of mild increases in the error and failure probability). Our algorithm combines a variant of the INW pseudorandom generator (STOC '94) with a new scheme for shifting and rounding the outputs of \(\mathsf{Est}\). We prove that modifying the outputs of \(\mathsf{Est}\) is necessary in this setting, and furthermore, our algorithm's randomness complexity is near-optimal in the case \(d \leq O(1)\). As an application, we give a randomness-efficient version of the Goldreich-Levin algorithm; our algorithm finds all Fourier coefficients with absolute value at least \(\theta\) of a function \(F \colon \{0, 1\}^n \to \{-1, 1\}\) using \(O(n \log n) \cdot \text{poly}(1/\theta)\) queries to \(F\) and \(O(n)\) random bits (independent of \(\theta\)), improving previous work by Bshouty et al. (JCSS '04).

## Not-so-abstract (for curious outsiders)

⚠️ *This summary might gloss over some important details.*

Suppose you have a randomized algorithm that estimates some numeric quantity, and you plan to use it \(k\) times. Suppose the estimation algorithm makes \(n\) coin tosses. This situation seems to call for \(nk\) coin tosses in total. In this paper, we show that you can actually get away with only making about \(n + k\) coin tosses if you use some tricks.

We posted a manuscript online in November 2016; I presented the paper at RANDOM in August 2018. The arXiv version and the ECCC version are identical. The RANDOM proceedings version is more compact and skips a lot of the material.

Expository material:

Slides from my presentation at RANDOM (August 2018). See also these slides from my longer presentation at Caltech's CS Theory Seminar (May 2017). In both cases, I recommend viewing the slides in Adobe Reader to get the animations to work.

Video from Adam's presentation at the Simons Institute "Proving and Using Pseudorandomness" workshop (March 2017).