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Poster
Stochastic and Adversarial Online Learning without Hyperparameters
Ashok Cutkosky · Kwabena A Boahen

Wed Dec 06 06:30 PM -- 10:30 PM (PST) @ Pacific Ballroom #53 #None
in Posters Wed »
Most online optimization algorithms focus on one of two things: performing well in adversarial settings by adapting to unknown data parameters (such as Lipschitz constants), typically achieving $O(\sqrt{T})$ regret, or performing well in stochastic settings where they can leverage some structure in the losses (such as strong convexity), typically achieving $O(\log(T))$ regret. Algorithms that focus on the former problem hitherto achieved $O(\sqrt{T})$ in the stochastic setting rather than $O(\log(T))$. Here we introduce an online optimization algorithm that achieves $O(\log^4(T))$ regret in a wide class of stochastic settings while gracefully degrading to the optimal $O(\sqrt{T})$ regret in adversarial settings (up to logarithmic factors). Our algorithm does not require any prior knowledge about the data or tuning of parameters to achieve superior performance.
Keywords: Hyperparameter Selection · Online Learning · Convex Optimization
[ Paper] 

Author Information

Ashok Cutkosky (Google)
Kwabena A Boahen (Stanford University)

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