<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Personal |</title><link>https://tiao.io/categories/personal/</link><atom:link href="https://tiao.io/categories/personal/index.xml" rel="self" type="application/rss+xml"/><description>Personal</description><generator>HugoBlox Kit (https://hugoblox.com)</generator><language>en-us</language><lastBuildDate>Wed, 06 Dec 2023 17:06:36 +0000</lastBuildDate><image><url>https://tiao.io/media/icon_hu_9c2a75fde2335590.png</url><title>Personal</title><link>https://tiao.io/categories/personal/</link></image><item><title>PhD Thesis Acknowledgements (Unabridged)</title><link>https://tiao.io/posts/phd-thesis-acknowledgements/</link><pubDate>Wed, 06 Dec 2023 17:06:36 +0000</pubDate><guid>https://tiao.io/posts/phd-thesis-acknowledgements/</guid><description>&lt;p&gt;With thanks&amp;hellip;&lt;/p&gt;
&lt;hr&gt;
&lt;p&gt;The full thesis text can be found below:&lt;/p&gt;
&lt;div class="pub-list-item view-citation" style="margin-bottom: 1rem"&gt;
&lt;i class="far fa-file-alt pub-icon" aria-hidden="true"&gt;&lt;/i&gt;
&lt;span class="article-metadata li-cite-author"&gt;
&lt;span &gt;&lt;a href="https://tiao.io/authors/me/"&gt;Louis Tiao&lt;/a&gt;&lt;/span&gt;
&lt;/span&gt;
(2023).
&lt;a href="https://tiao.io/publications/phd-thesis/" class="underline"&gt;Probabilistic Machine Learning in the Age of Deep Learning: New Perspectives for Gaussian Processes, Bayesian Optimization and Beyond (PhD Thesis)&lt;/a&gt;.
&lt;div class="flex flex-wrap space-x-3"&gt;
&lt;a class="hb-attachment-link hb-attachment-link-small" href="https://tiao.io/publications/phd-thesis/phd-thesis-louis-tiao.pdf" &gt;
&lt;svg style="height: 1em" class='inline-block' xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"&gt;&lt;path fill="none" stroke="currentColor" stroke-linecap="round" stroke-linejoin="round" stroke-width="1.5" d="M19.5 14.25v-2.625a3.375 3.375 0 0 0-3.375-3.375h-1.5A1.125 1.125 0 0 1 13.5 7.125v-1.5a3.375 3.375 0 0 0-3.375-3.375H8.25m0 12.75h7.5m-7.5 3H12M10.5 2.25H5.625c-.621 0-1.125.504-1.125 1.125v17.25c0 .621.504 1.125 1.125 1.125h12.75c.621 0 1.125-.504 1.125-1.125V11.25a9 9 0 0 0-9-9"/&gt;&lt;/svg&gt;
PDF
&lt;/a&gt;
&lt;a class="hb-attachment-link hb-attachment-link-small" href="https://hdl.handle.net/2123/32803" target="_blank" rel="noopener"&gt;
&lt;svg style="height: 1em" class='inline-block' xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"&gt;&lt;path fill="none" stroke="currentColor" stroke-linecap="round" stroke-linejoin="round" stroke-width="1.5" d="M19.5 14.25v-2.625a3.375 3.375 0 0 0-3.375-3.375h-1.5A1.125 1.125 0 0 1 13.5 7.125v-1.5a3.375 3.375 0 0 0-3.375-3.375H8.25m2.25 0H5.625c-.621 0-1.125.504-1.125 1.125v17.25c0 .621.504 1.125 1.125 1.125h12.75c.621 0 1.125-.504 1.125-1.125V11.25a9 9 0 0 0-9-9"/&gt;&lt;/svg&gt;
USyd Library
&lt;/a&gt;
&lt;a class="hb-attachment-link hb-attachment-link-small" href="https://tiao.io/posts/phd-thesis-acknowledgements/" &gt;
&lt;svg style="height: 1em" class='inline-block' xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"&gt;&lt;path fill="none" stroke="currentColor" stroke-linecap="round" stroke-linejoin="round" stroke-width="1.5" d="M19.5 14.25v-2.625a3.375 3.375 0 0 0-3.375-3.375h-1.5A1.125 1.125 0 0 1 13.5 7.125v-1.5a3.375 3.375 0 0 0-3.375-3.375H8.25m2.25 0H5.625c-.621 0-1.125.504-1.125 1.125v17.25c0 .621.504 1.125 1.125 1.125h12.75c.621 0 1.125-.504 1.125-1.125V11.25a9 9 0 0 0-9-9"/&gt;&lt;/svg&gt;
Full Acknowledgements
&lt;/a&gt;
&lt;/div&gt;
&lt;/div&gt;</description></item></channel></rss>