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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Fair and Diverse DPP-based Data Summarization · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1802.04023/</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/1802.04023/feed.xml" rel="self" type="application/rss+xml" /><item><title>Fair and Diverse DPP-based Data Summarization — mentioned by lightcatcher</title><link>https://news.ycombinator.com/item?id=25297624</link><guid isPermaLink="false">urn:hackerpapers:mention:1802.04023:25297624</guid><pubDate>Fri, 04 Dec 2020 00:50:00 GMT</pubDate><description>&lt;p&gt;Fairness, Accountability, Transparency (aka FAT) is a real sub-field of machine learning, that is as technical as any other machine learning sub-field in my opinion. It is not a PR stunt. I&amp;#x27;ve published at top ML conference a paper about some CUDA kernels I wrote to accelerate training a class of RNNs, so I feel have technical grounding to make these claims. Here are some FAT papers I&amp;#x27;ve enjoyed: https://arxiv.org/abs/1806.08010 https://arxiv.org/abs/1802.04023 https://arxiv.org/abs/1803.04383 (won best paper award at ICML 2018) Many FAT papers are published at NeurIPS or ICML (generally…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1802.04023/"&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>
