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+in+a+linear+number+of+oracle+steps+and+the+information-theoretic+rate.+Our+approach+formulates+tensor+completion+as+a+convex+optimization+problem+constrained+using+a+gauge-based+tensor+norm%252C+which+is+defined+in+a+way+that+allows+the+use+of+integer+linear+optimization+to+solve+linear+separation+problems+over+the+unit-ball+in+this+new+norm.+Adaptations+based+on+this+insight+are+incorporated+into+a+Frank-Wolfe+variant+to+build+our+algorithm.+We+show+our+algorithm+scales-well+using+numerical+experiments+on+tensors+with+up+to+ten+million+entries.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.bcf5a73b97e8603ff114ed27ba7162b5%3frik%3dopEB8RwBGqe9wQ%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Tensor+Completion+Model&FORM=IRPRST&ck=F88D9238722C845B57B2084D041E9E12&selectedIndex=17&itb=0)
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