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<rss version="2.0"><channel><description>Algorithms, predictions, privacy. &#xA;https://theory.stanford.edu/~sergei/</description><link>https://bsky.app/profile/vsergei.bsky.social</link><title>@vsergei.bsky.social - Sergei Vassilvitskii</title><item><link>https://bsky.app/profile/vsergei.bsky.social/post/3li5drsxpds2y</link><description>Synthetic Data is all the rage in LLM training, but why does it work? In arxiv.org/abs/2502.08924 we show how to analyze this question through the lens of boosting. Unlike boosting, however, our assumptions on the data and the learning method are inverted.&#xA;https://arxiv.org/abs/2502.08924</description><pubDate>14 Feb 2025 13:48 +0000</pubDate><guid isPermaLink="false">at://did:plc:wspv5m5jjvb5qei2mtgzcbng/app.bsky.feed.post/3li5drsxpds2y</guid></item></channel></rss>