Online to PAC Conversion

Online-to-PAC conversion research focuses on bridging the gap between online learning algorithms, which process data sequentially, and PAC (Probably Approximately Correct) learning, which aims for generalization guarantees on unseen data. Current research explores efficient conversion methods, investigating the relationship between online learning regret and PAC generalization bounds, often employing techniques like regret analysis and leveraging properties of data distributions such as exchangeability or mixing processes. This work is significant for improving the theoretical understanding of learning algorithms and for developing more robust and efficient machine learning models, particularly in scenarios with non-i.i.d. data or limited computational resources.