Learning from Biased Data: A Semi-Parametric Approach

Patrice Bertail · Stephan Clémençon · Yannick Guyonvarch · Nathan NOIRY

Keywords: [ Applications -> Fairness, Accountability, and Transparency ] [ Theory ] [ Algorithms -> Clustering; Applications -> Hardware and Systems; Applications -> Privacy, Anonymity, and Security ]

Abstract: We consider risk minimization problems where the (source) distribution $P_S$ of the training observations $Z_1, \ldots, Z_n$ differs from the (target) distribution $P_T$ involved in the risk that one seeks to minimize. Under the natural assumption that $P_S$ dominates $P_T$, \textit{i.e.} $P_T< \! \!

Chat is not available.