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Poster
Characterizing Implicit Bias in Terms of Optimization Geometry
Suriya Gunasekar · Jason Lee · Daniel Soudry · Nati Srebro
We study the bias of generic optimization methods, including Mirror Descent, Natural Gradient Descent and Steepest Descent with respect to different potentials and norms, when optimizing underdetermined linear models or separable linear classification problems. We ask the question of whether the global minimum (among the many possible global minima) reached by optimization can be characterized in terms of the potential or norm, and indecently of hyper-parameter choices, such as stepsize and momentum.
Author Information
Suriya Gunasekar (Toyota Technological Institute at Chicago)
Jason Lee (University of Southern California)
Daniel Soudry (Technion)
Nati Srebro (Toyota Technological Institute at Chicago)
Related Events (a corresponding poster, oral, or spotlight)
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2018 Oral: Characterizing Implicit Bias in Terms of Optimization Geometry »
Thu. Jul 12th 11:50 AM -- 12:00 PM Room A9
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