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The Ethereal
Gradient-Variation Regret Bounds for Unconstrained Online Learning
April 13, 2026 ยท Grace Period ยท + Add venue
Authors
Yuheng Zhao, Andrew Jacobsen, Nicolรฒ Cesa-Bianchi, Peng Zhao
arXiv ID
2604.11151
Category
cs.LG: Machine Learning
Cross-listed
stat.ML
Citations
0
Abstract
We develop parameter-free algorithms for unconstrained online learning with regret guarantees that scale with the gradient variation $V_T(u) = \sum_{t=2}^T \|\nabla f_t(u)-\nabla f_{t-1}(u)\|^2$. For $L$-smooth convex loss, we provide fully-adaptive algorithms achieving regret of order $\widetilde{O}(\|u\|\sqrt{V_T(u)} + L\|u\|^2+G^4)$ without requiring prior knowledge of comparator norm $\|u\|$, Lipschitz constant $G$, or smoothness $L$. The update in each round can be computed efficiently via a closed-form expression. Our results extend to dynamic regret and find immediate implications to the stochastically-extended adversarial (SEA) model, which significantly improves upon the previous best-known result [Wang et al., 2025].
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