2 papers
cs.LG2026
Agile Online Model Selection: Resolving Adaptation Lag via Safeguarded Large Learning Rates
Kei Takemura, Ryuta Matsuno, Keita Sakuma
Maintaining predictive accuracy in non-stationary environments requires online model selection to adapt autonomously to unknown distribution shifts. However, existing tuning-free a…
cs.LG2025
Improved Impossible Tuning and Lipschitz-Adaptive Universal Online Learning with Gradient Variations
Kei Takemura, Ryuta Matsuno, Keita Sakuma
A central goal in online learning is to achieve adaptivity to unknown problem characteristics, such as environmental changes captured by gradient variation (GV), function curvature…