PAC Learning

A classical framework in computational learning theory that characterizes the conditions under which a learner obtains an approximately correct hypothesis with high probability from a finite number of samples. Continual PAC learning extends it to settings where the task distribution changes over time and the learner continuously receives new data, requiring the model to adapt to new tasks without forgetting old capabilities.