<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>News on Laurence A. F. Park</title><link>https://lapark.github.io/news/</link><description>Recent content in News on Laurence A. F. Park</description><generator>Hugo</generator><language>en-us</language><copyright>This page, its contents and style, are the responsibility of the author and do not necessarily represent the views, policies or opinions of Western Sydney University. The header image Memorial to Folly is unofficial Fan Content permitted under the Fan Content Policy. Not approved/endorsed by Wizards. Portions of the materials used are property of Wizards of the Coast. ©Wizards of the Coast LLC. All other content &amp;copy; Laurence Park</copyright><lastBuildDate>Wed, 01 Jan 2025 00:00:00 +0000</lastBuildDate><atom:link href="https://lapark.github.io/news/index.xml" rel="self" type="application/rss+xml"/><item><title>Now Recruiting</title><link>https://lapark.github.io/news/recruiting/</link><pubDate>Tue, 14 Jan 2020 11:25:05 -0400</pubDate><guid>https://lapark.github.io/news/recruiting/</guid><description>&lt;p&gt;I am now recruiting students willing to undertake a Masters or PhD
degree in the fields of Information Retrieval, Data Mining or Machine
Learning. Interested students should contact me and provide a current
resume outlining relevant academic and professional history.&lt;/p&gt;</description></item><item><title>How Confident Is Your Multi-Label Classifier? Estimating Expected Accuracy from Label Distributions</title><link>https://lapark.github.io/news/estimating-multilabel-expected-accuracy/</link><pubDate>Wed, 01 Jan 2025 00:00:00 +0000</pubDate><guid>https://lapark.github.io/news/estimating-multilabel-expected-accuracy/</guid><description>&lt;p&gt;When a multi-label classifier makes a prediction — say, flagging a patient record for Diabetes, Hypertension, &lt;em&gt;and&lt;/em&gt; COVID-19 — how confident should you be? This question is harder than it looks. In a single-label setting, the probability score attached to a prediction is a straightforward measure of confidence. In the multi-label world, it gets complicated fast.&lt;/p&gt;
&lt;p&gt;A new paper by &lt;strong&gt;Laurence A. F. Park&lt;/strong&gt; (Western Sydney University) and &lt;strong&gt;Jesse Read&lt;/strong&gt; (École Polytechnique) takes a rigorous look at this problem, testing seven candidate functions for estimating expected accuracy from a multi-label probability distribution — and finding clear winners depending on how accuracy is measured.&lt;/p&gt;</description></item><item><title>Data Science Research Group</title><link>https://lapark.github.io/news/researchgroup/</link><pubDate>Tue, 14 Jan 2020 11:25:05 -0400</pubDate><guid>https://lapark.github.io/news/researchgroup/</guid><description>&lt;p&gt;If you have a new idea that will advance the field of data science,
why not present your idea in one of our Data Science research group seminars. If
you are interested, contact me to get the process started.&lt;/p&gt;</description></item><item><title>textIR 0.5 Released</title><link>https://lapark.github.io/news/textir05/</link><pubDate>Thu, 14 Apr 2011 11:25:05 -0400</pubDate><guid>https://lapark.github.io/news/textir05/</guid><description>&lt;p&gt;The latest version of textIR has been released. The new features include
indexing, retrieval and topic modelling of text document sets. More information
can be found at &lt;a href="https://www.scm.uws.edu.au/~lapark/textIR"&gt;https://www.scm.uws.edu.au/~lapark/textIR&lt;/a&gt;.&lt;/p&gt;</description></item></channel></rss>