Feb. 6, 2024, 5:43 a.m. | Zhiyu Zhang David Bombara Heng Yang

cs.LG updates on arXiv.org arxiv.org

Online learning is not always about memorizing everything. Since the future can be statistically very different from the past, a critical challenge is to gracefully forget the history while new data comes in. To formalize this intuition, we revisit the classical notion of discounted regret using recently developed techniques in adaptive online learning. Our main result is a new algorithm that adapts to the complexity of both the loss sequence and the comparator, improving the widespread non-adaptive algorithm - gradient …

challenge cs.lg data everything future history intuition notion online learning prediction stat.ml

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