Unbounded Loss

Unbounded loss in machine learning addresses the challenge of training models with loss functions that can take on arbitrarily large values, a departure from the common assumption of bounded losses. Current research focuses on developing robust algorithms and theoretical guarantees for deep learning and online learning settings with unbounded losses, exploring techniques like truncation, PAC-Bayes bounds, and specialized algorithms for adversarial bandits. This research is significant because it expands the applicability of machine learning to real-world problems with heavy-tailed data or inherently unbounded loss functions, improving the robustness and reliability of models in these scenarios.