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<oembed><version>1.0</version><provider_name>Microsoft Research</provider_name><provider_url>https://www.microsoft.com/en-us/research</provider_url><author_name>Prateek Jain</author_name><author_url>https://www.microsoft.com/en-us/research/people/prajain/</author_url><title>Universal Matrix Completion - Microsoft Research</title><type>rich</type><width>600</width><height>338</height><html>&lt;blockquote class="wp-embedded-content" data-secret="QEaQu7HSQQ"&gt;&lt;a href="https://www.microsoft.com/en-us/research/publication/universal-matrix-completion/"&gt;Universal Matrix Completion&lt;/a&gt;&lt;/blockquote&gt;&lt;iframe sandbox="allow-scripts" security="restricted" src="https://www.microsoft.com/en-us/research/publication/universal-matrix-completion/embed/#?secret=QEaQu7HSQQ" width="600" height="338" title="&#x201C;Universal Matrix Completion&#x201D; &#x2014; Microsoft Research" data-secret="QEaQu7HSQQ" frameborder="0" marginwidth="0" marginheight="0" scrolling="no" class="wp-embedded-content"&gt;&lt;/iframe&gt;&lt;script type="text/javascript"&gt;
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</html><description>The problem of low-rank matrix completion has recently generated a lot of interest leading to several results that offer exact solutions to the problem. However, in order to do so, these methods make assumptions that can be quite restrictive in practice. More specifically, the methods assume that: a) the observed indices are sampled uniformly at [&hellip;]</description></oembed>
