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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Estimating Uncertainty Online Against an Adversary · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1607.03594/</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/1607.03594/feed.xml" rel="self" type="application/rss+xml" /><item><title>Estimating Uncertainty Online Against an Adversary — mentioned by volodia</title><link>https://news.ycombinator.com/item?id=13587428</link><guid isPermaLink="false">urn:hackerpapers:mention:1607.03594:13587428</guid><pubDate>Tue, 07 Feb 2017 06:02:02 GMT</pubDate><description>&lt;p&gt;That&amp;#x27;s an interesting discussion! Having read the Vovk papers, this blog post definitely presents things much more clearly. The original papers often don&amp;#x27;t adhere to the standard definition/lemma/proof style of mathematical exposition, which makes them really hard to follow. It&amp;#x27;s also an interesting coincidence that this story on the front page today. I&amp;#x27;m giving a talk tomorrow at AAAI on some work that extends this theory. We show how to do uncertainty estimation (e.g. calibrated probabilities for ML classifiers) under fully adversarial assumptions (input data can be chosen by an adversary).…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1607.03594/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.LG</category></item></channel></rss>
