<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>XAI on Jannik P. Roth</title><link>https://jannik-roth.github.io/tags/xai/</link><description>Recent content in XAI on Jannik P. Roth</description><generator>Hugo -- 0.147.2</generator><language>en</language><lastBuildDate>Fri, 20 Dec 2024 00:00:00 +0000</lastBuildDate><atom:link href="https://jannik-roth.github.io/tags/xai/index.xml" rel="self" type="application/rss+xml"/><item><title>Protocol to calculate and compare exact Shapley values for different kernels in support vector machine models using binary features</title><link>https://jannik-roth.github.io/papers/2024_star/</link><pubDate>Fri, 20 Dec 2024 00:00:00 +0000</pubDate><guid>https://jannik-roth.github.io/papers/2024_star/</guid><description>A protocol to calculate and compare exact Shapley values for support vector machine models with commonly used kernels and binary input features is developed. Published in STAR Protocols, 2024</description></item><item><title>Machine learning models with distinct Shapley value explanations decouple feature attribution and interpretation for chemical compound predictions</title><link>https://jannik-roth.github.io/papers/2024_exact_shapley/</link><pubDate>Wed, 21 Aug 2024 00:00:00 +0000</pubDate><guid>https://jannik-roth.github.io/papers/2024_exact_shapley/</guid><description>A method for the exact calculation of Shapley Values for Support Vector Machines is introduced and tested for compound prediction tasks. Published in Cell Reports Physical Science, 2024</description></item></channel></rss>