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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2026
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2602.08151 |
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| _version_ | 1866917258106765312 |
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| author | Freund, Yoav Harvey, Nicholas J. A. Portella, Victor S. Qi, Yabing Wang, Yu-Xiang |
| author_facet | Freund, Yoav Harvey, Nicholas J. A. Portella, Victor S. Qi, Yabing Wang, Yu-Xiang |
| contents | We consider the problem of prediction with expert advice for ``easy'' sequences. We show that a variant of NormalHedge enjoys a second-order $ε$-quantile regret bound of $O\big(\sqrt{V_T \log(V_T/ε)}\big) $ when $V_T > \log N$, where $V_T$ is the cumulative second moment of instantaneous per-expert regret averaged with respect to a natural distribution determined by the algorithm. The algorithm is motivated by a continuous time limit using Stochastic Differential Equations. The discrete time analysis uses self-concordance techniques. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_08151 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A second order regret bound for NormalHedge Freund, Yoav Harvey, Nicholas J. A. Portella, Victor S. Qi, Yabing Wang, Yu-Xiang Machine Learning We consider the problem of prediction with expert advice for ``easy'' sequences. We show that a variant of NormalHedge enjoys a second-order $ε$-quantile regret bound of $O\big(\sqrt{V_T \log(V_T/ε)}\big) $ when $V_T > \log N$, where $V_T$ is the cumulative second moment of instantaneous per-expert regret averaged with respect to a natural distribution determined by the algorithm. The algorithm is motivated by a continuous time limit using Stochastic Differential Equations. The discrete time analysis uses self-concordance techniques. |
| title | A second order regret bound for NormalHedge |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2602.08151 |