All reports by Author Ilias Diakonikolas:

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TR22-178
| 8th December 2022
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Ilias Diakonikolas, Christos Tzamos, Daniel Kane#### A Strongly Polynomial Algorithm for Approximate Forster Transforms and its Application to Halfspace Learning

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TR20-140
| 14th September 2020
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Ilias Diakonikolas, Themis Gouleakis, Daniel Kane, John Peebles, Eric Price#### Optimal Testing of Discrete Distributions with High Probability

Ilias Diakonikolas, Christos Tzamos, Daniel Kane

The Forster transform is a method of regularizing a dataset

by placing it in {\em radial isotropic position}

while maintaining some of its essential properties.

Forster transforms have played a key role in a diverse range of settings

spanning computer science and functional analysis. Prior work had given

{\em ...
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Ilias Diakonikolas, Themis Gouleakis, Daniel Kane, John Peebles, Eric Price

We study the problem of testing discrete distributions with a focus on the high probability regime.

Specifically, given samples from one or more discrete distributions, a property $\mathcal{P}$, and

parameters $0< \epsilon, \delta <1$, we want to distinguish {\em with probability at least $1-\delta$}

whether these distributions satisfy $\mathcal{P}$ ...
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