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Optimizing Black-box Metrics with Adaptive Surrogates
Qijia Jiang · Olaoluwa Adigun · Harikrishna Narasimhan · Mahdi Milani Fard · Maya Gupta

Wed Jul 15 08:00 AM -- 08:45 AM & Wed Jul 15 09:00 PM -- 09:45 PM (PDT) @ Virtual #None

We address the problem of training models with black-box and hard-to-optimize metrics by expressing the metric as a monotonic function of a small number of easy-to-optimize surrogates. We pose the training problem as an optimization over a relaxed surrogate space, which we solve by estimating local gradients for the metric and performing inexact convex projections. We analyze gradient estimates based on finite differences and local linear interpolations, and show convergence of our approach under smoothness assumptions with respect to the surrogates. Experimental results on classification and ranking problems verify the proposal performs on par with methods that know the mathematical formulation, and adds notable value when the form of the metric is unknown.

Author Information

Qijia Jiang (Stanford University)
Olaoluwa Adigun (University of Southern California Los Angeles)
Harikrishna Narasimhan (Google Research)
Mahdi Milani Fard (Google)
Maya Gupta (Google)

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