The study explores the Stochastically Extended Adversarial (SEA) model, bridging stochastic and adversarial online convex optimization. It introduces optimistic Online Mirror Descent (OMD) for the SEA model with smooth expected loss functions. The research provides regret bounds for convex, strongly convex, and exp-concave functions. Results show improved bounds compared to previous works by Sachs et al. (2022). The analysis includes assumptions on gradient norms, domain boundedness, maximal variance, smoothness of expected functions, convexity, and strong convexity. The study presents novel results for exp-concave functions not previously explored.
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arxiv.org
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by Sijia Chen,Y... ב- arxiv.org 03-19-2024
https://arxiv.org/pdf/2302.04552.pdfשאלות מעמיקות