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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Deep Optimization for Spectrum Repacking · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1706.03304/</link><description>Collected HN comments and submissions linking this paper.</description><language>en</language><lastBuildDate>Mon, 02 Nov 2026 11:16:22 GMT</lastBuildDate><atom:link href="https://hackerpaper.kaipfister.de/p/1706.03304/feed.xml" rel="self" type="application/rss+xml" /><item><title>Deep Optimization for Spectrum Repacking — mentioned by lpage</title><link>https://news.ycombinator.com/item?id=30251180</link><guid isPermaLink="false">urn:hackerpapers:mention:1706.03304:30251180</guid><pubDate>Mon, 07 Feb 2022 21:08:14 GMT</pubDate><description>&lt;p&gt;We&amp;#x27;re simply standing on the shoulders of giants for this one. Paul Milgrom and Bob Wilson, winners of the 2020 Prize in Economic Sciences, are responsible for the underlying theory and commercialization in other industries. My advisor, Preston McAfee, introduced me to the concept (Milgrom&amp;#x27;s book was the auction theory text) and was largely responsible for bringing mechanism design to ad markets. Lots of folks are applying machine learning to accelerating discrete optimization problems, and an advisor of ours, Kevin Leyton-Brown [2], pioneered applying it to combinatorial auctions for…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1706.03304/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.AI</category></item></channel></rss>
