Small Loss

"Small loss" research focuses on developing algorithms and models that are robust to noisy data or achieve high performance even with minimal optimal cost. Current efforts concentrate on improving sample selection techniques, designing novel loss functions (like regroup median loss), and developing algorithms with small-loss bounds for reinforcement learning and online learning settings. This work is significant because it enhances the efficiency and reliability of machine learning models, particularly in scenarios with limited data or high levels of noise, leading to improved performance in various applications.