Agile Online Model Selection: Resolving Adaptation Lag via Safeguarded Large Learning Rates
Maintaining predictive accuracy in non-stationary environments requires online model selection to adapt autonomously to unknown distribution shifts. However, existing tuning-free algorithms face a fundamental trade-off between robustness and agility. Specifically, to ensure dynamic regret bounds, they must restrict learning rates to small constants (e.g., $O(1)$). This restriction inevitably causes significant adaptation lag during abrupt changes. To resolve this, we propose a novel optimistic online mirror descent that utilizes safeguarded large learning rates up to $Θ(T)$, where $T$ is the number of rounds. Our key technical contribution is a post-hoc penalty mechanism that dynamically ...
| Authors | Kei Takemura, Ryuta Matsuno, Keita Sakuma |
|---|---|
| Primary category | cs.LG |
| Published | 26 May 2026 |
| Last updated at source | 1 June 2026 |
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