Unifying and Optimizing Data Values for Selection via Sequential Decision-Making

ArXi:2502.04554v2 Announce Type: replace Data selection has emerged as a crucial downstream application of data valuation, yet the theoretical foundations for using data values in selection remain underexplored. We reformulate data selection as a sequential decision-making problem where the optimal selection sequence arises from dynamic programming, and data values can be understood as encodings of this optimal sequence.