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| | jeremykun.com
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| | Hard to believe Sanjeev Arora and his coauthors consider it"a basic tool [that should be] taught to all algorithms students together with divide-and-conquer, dynamic programming, and random sampling."Christos Papadimitriou calls it"so hard to believe that it has been discovered five times and forgotten." It has formed the basis of algorithms inmachine learning, optimization, game theory,
| | algorithmsoup.wordpress.com
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| | The ``probabilistic method'' is the art of applying probabilistic thinking to non-probabilistic problems. Applications of the probabilistic method often feel like magic. Here is my favorite example: Theorem (Erdös, 1965). Call a set $latex {X}&fg=000000$ sum-free if for all $latex {a, b \in X}&fg=000000$, we have $latex {a + b \not\in X}&fg=000000$. For any finite...
| | nickhar.wordpress.com
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| | 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...
| | blog.ml.cmu.edu
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| The latest news and publications regarding machine learning, artificial intelligence or related, brought to you by the Machine Learning Blog, a spinoff of the Machine Learning Department at Carnegie Mellon University.