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cs.LG2025
Source Component Shift Adaptation via Offline Decomposition and Online Mixing Approach
Ryuta Matsuno
This paper addresses source component shift adaptation, aiming to update predictions adapting to source component shifts for incoming data streams based on past training data. Exis…
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…