<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Machine Learning |</title><link>https://www.khaichiong.com/tags/machine-learning/</link><atom:link href="https://www.khaichiong.com/tags/machine-learning/index.xml" rel="self" type="application/rss+xml"/><description>Machine Learning</description><generator>HugoBlox Kit (https://hugoblox.com)</generator><language>en-us</language><lastBuildDate>Sun, 26 Apr 2026 00:00:00 +0000</lastBuildDate><image><url>https://www.khaichiong.com/media/icon_hu_da05098ef60dc2e7.png</url><title>Machine Learning</title><link>https://www.khaichiong.com/tags/machine-learning/</link></image><item><title>Learning Heterogeneity from Unstructured Data: An Application to Chatbot Personalization</title><link>https://www.khaichiong.com/publications/chatbot-personalization-unstructured-data/</link><pubDate>Sun, 26 Apr 2026 00:00:00 +0000</pubDate><guid>https://www.khaichiong.com/publications/chatbot-personalization-unstructured-data/</guid><description>&lt;p&gt;Working paper.&lt;/p&gt;</description></item><item><title>Random Projection Estimation of Discrete-Choice Models with Large Choice Sets</title><link>https://www.khaichiong.com/publications/random-projection-estimation-discrete-choice/</link><pubDate>Tue, 01 Jan 2019 00:00:00 +0000</pubDate><guid>https://www.khaichiong.com/publications/random-projection-estimation-discrete-choice/</guid><description>&lt;p&gt;Chiong and Shum tackle the computational challenges posed by discrete-choice
models with large choice sets. Their random projection estimator dramatically
reduces dimensionality, allowing researchers to analyze complex choice data
efficiently. The approach is particularly useful for modern marketing and
transportation applications involving many alternatives.&lt;/p&gt;</description></item></channel></rss>