Poster
Implicit Regularization in Matrix Factorization
Suriya Gunasekar · Blake Woodworth · Srinadh Bhojanapalli · Behnam Neyshabur · Nati Srebro
We study implicit regularization when optimizing an underdetermined quadratic objective over a matrix $X$ with gradient descent on a factorization of X. We conjecture and provide empirical and theoretical evidence that with small enough step sizes and initialization close enough to the origin, gradient descent on a full dimensional factorization converges to the minimum nuclear norm solution.
Author Information
Suriya Gunasekar (TTI Chicago)
Blake Woodworth (TTI-Chicago)
Srinadh Bhojanapalli (Google Research)
Behnam Neyshabur (New York University)
Nati Srebro (TTI-Chicago)
Related Events (a corresponding poster, oral, or spotlight)
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2017 Spotlight: Implicit Regularization in Matrix Factorization »
Tue Dec 5th 11:40 -- 11:45 AM Room Hall C
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