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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Minimax Policies for Combinatorial Prediction Games · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1105.4871/</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/1105.4871/feed.xml" rel="self" type="application/rss+xml" /><item><title>Minimax Policies for Combinatorial Prediction Games — mentioned by noelwelsh</title><link>https://news.ycombinator.com/item?id=2832447</link><guid isPermaLink="false">urn:hackerpapers:mention:1105.4871:2832447</guid><pubDate>Mon, 01 Aug 2011 15:01:16 GMT</pubDate><description>&lt;p&gt;I agree with the general thrust of what you&amp;#x27;re saying -- it&amp;#x27;s a hard problem and no amount of algorithmic finesse can get around the basic problem of the exponentially growing space. In the context of this post, however, I think you can still do a lot better than A/B testing or MVT :). Here&amp;#x27;s a paper that I think (I haven&amp;#x27;t read it in detail yet) addresses the problem you&amp;#x27;re talking about: http://arxiv.org/pdf/1105.4871v1 Another approach would be to model interactions as a (undirected?) Bayesian network and try to learn the structure of the network from data. I&amp;#x27;ve had reasonable success…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1105.4871/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>stat.ML</category></item></channel></rss>
