##### Department of Mathematics,

University of California San Diego

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### Math 278B - Mathematics of Information, Data, and Signals Seminar:

## Piotr Indyk

#### MIT

## Learning-Based Sampling and Streaming

##### Abstract:

Classical algorithms typically provide "one size fits all" performance, and do not leverage properties or patterns in their inputs. A recent line of work aims to address this issue by developing algorithms that use machine learning predictions to improve their performance. In this talk I will present two examples of this type, in the context of streaming and sampling algorithms. In particular, I will show how to use machine learning predictions to improve the performance of (a) low-memory streaming algorithms for frequency estimation (ICLRâ€™19), and (b) sampling algorithms for estimating the support size of a distribution (ICLRâ€™21). Both algorithms use an ML-based predictor that, given a data item, estimates the number of times the item occurs in the input data set. \\ \\ The talk will cover material from papers co-authored with T Eden, CY Hsu, D Katabi, S Narayanan, R Rubinfeld, S Silwal, T Wagner and A Vakilian.

Host: Rayan Saab

### June 10, 2021

### 11:30 AM

### Zoom link: https://msu.zoom.us/j/96421373881 (passcode: first prime number $>$ 100)

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