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- W3208366567 abstract "We observe that many system policies that make threshold decisions involving a resource (e.g., time, memory, cores) naturally reveal additional, or implicit feedback. For example, if a system waits X min for an event to occur, then it automatically learns what would have happened if it waited < X min, because time has a cumulative property. This feedback tells us about alternative decisions, and can be used to improve the system policy. However, leveraging implicit feedback is difficult because it tends to be one-sided or incomplete, and may depend on the outcome of the event. As a result, existing practices for using feedback, such as simply incorporating it into a data-driven model, suffer from bias." @default.
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- W3208366567 date "2021-11-01" @default.
- W3208366567 modified "2023-10-05" @default.
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- W3208366567 doi "https://doi.org/10.1145/3472883.3487001" @default.
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