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PiRank: Scalable Learning To Rank via Differentiable Sorting
[article]
2021
arXiv
pre-print
A key challenge with machine learning approaches for ranking is the gap between the performance metrics of interest and the surrogate loss functions that can be optimized with gradient-based methods. This gap arises because ranking metrics typically involve a sorting operation which is not differentiable w.r.t. the model parameters. Prior works have proposed surrogates that are loosely related to ranking metrics or simple smoothed versions thereof, and often fail to scale to real-world
arXiv:2012.06731v2
fatcat:amtphimdijcodefysa67wxv2mu