Task Boundary

Task boundary research addresses the challenges of machine learning in scenarios where data streams lack clear distinctions between tasks or exhibit noisy labels and gradual shifts in data distributions. Current efforts focus on developing algorithms and model architectures (e.g., neural ensembles, conformal prediction) that enable continual learning, robust adaptation to changing environments, and accurate uncertainty quantification, even with fuzzy task boundaries. This work is crucial for building more reliable and adaptable AI systems, particularly in applications like robotics, medical image analysis, and online learning where data is inherently dynamic and often imperfect.