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University of California
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Excluding features based on manipulability can lead to suboptimal predictions, revealing a critical flaw in standard feature selection practices.
Forget global worst-case guarantees – this new multi-objective learning method actually adapts to distribution shifts and delivers unbiased predictions in dynamic environments.
