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lucatrevisan.wordpress.com | ||
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blogs.princeton.edu
[latexpage] Sum of squares optimization is an active area of research at the interface of algorithmic algebra and convex optimization. Over the last decade, it has made significant impact on both d... |
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francisbach.com
[AI summary] This text discusses the scaling laws of optimization in machine learning, focusing on asymptotic expansions for both strongly convex and non-strongly convex cases. It covers the derivation of performance bounds using techniques like Laplace's method and the behavior of random minimizers. The text also explains the 'weird' behavior observed in certain plots, where non-strongly convex bounds become tight under specific conditions. The analysis connects theoretical results to practical considerations in optimization algorithms. |
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nickhar.wordpress.com
The algorithm for probabilistically embedding metric spaces into trees has numerous theoretical applications. It is a key tool in the design of many approximation algorithms and online algorithms. Today we will illustrate the usefulness of these trees in designing an algorithm for the online Steiner tree problem. 1. Online Steiner Tree Let $latex {G=(V,E)}&fg=000000$ be... |
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achyutjoshi.github.io
Many would reckon that Machine Learning is now the new oil these days. And I would most likely support that. Personally I got exposed to the world of ML in m... |
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