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Smoothness, Low Noise and Fast Rates
Nati Srebro · Karthik Sridharan · Ambuj Tewari

Tue Dec 07 12:00 AM -- 12:00 AM (PST) @

We establish an excess risk bound of O(H Rn^2 + sqrt{H L*} Rn) for ERM with an H-smooth loss function and a hypothesis class with Rademacher complexity Rn, where L* is the best risk achievable by the hypothesis class. For typical hypothesis classes where Rn = sqrt{R/n}, this translates to a learning rate of ̃ O(RH/n) in the separable (L* = 0) case and O(RH/n + sqrt{L* RH/n}) more generally. We also provide similar guarantees for online and stochastic convex optimization of a smooth non-negative objective.

Author Information

Nati Srebro (TTI-Chicago)
Karthik Sridharan (University of Pennsylvania)
Ambuj Tewari (University of Michigan)

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