Distributionally robust learning-to-rank under the wasserstein metric

HIGHLIGHTS

• who: Shahabeddin Sotudian et al. from the Division of Systems Engineering, Department of Electrical and Computer Engineering, Boston have published the paper: Distributionally robust learning-to-rank under the Wasserstein metric, in the Journal: PLOS ONE of 30/Oct/2022
• what: The aim of document retrieval is to rank a set of documents by their relevance to a query. The authors seek to infuse robustness into LTR problems through the DRO framework. More importantly, the authors evaluate the model to verify its robustness against various types of attacks including adversarial attacks and label attacks, showing . . .
  

   

  

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