Researchers have identified an ambiguity in the term "machine unlearning," noting it encompasses two distinct processes: \untraining and \unlearning. \Untraining aims to remove the influence of specific data points from model training, while \unlearning seeks to eliminate broader underlying distributions or concepts represented by those data points. This distinction is crucial for developers as it clarifies research metrics and directions, paving the way for more precise algorithm comparisons and advancements in data privacy and compliance.
Read the full article at arXiv cs.LG (ML)
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