## Abstract

We present a detailed study of H-consistency bounds for score-based ranking. These are upper bounds on the target loss estimation error of a predictor in a hypothesis set H, expressed in terms of the surrogate loss estimation error of that predictor. We will show that both in the general pairwise ranking scenario and in the bipartite ranking scenario, there are no meaningful H-consistency bounds for most hypothesis sets used in practice including the family of linear models and that of the neural networks, which satisfy the equicontinuous property with respect to the input. To come up with ranking surrogate losses with theoretical guarantees, we show that a natural solution consists of resorting to a pairwise abstention loss in the general pairwise ranking scenario, and similarly, a bipartite abstention loss in the bipartite ranking scenario, to abstain from making predictions at some limited cost c. For surrogate losses of these abstention loss functions, we give a series of H-consistency bounds for both the family of linear functions and that of neural networks with one hidden-layer. Our experimental results illustrate the effectiveness of ranking with abstention.

Original language | English (US) |
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Pages (from-to) | 23743-23802 |

Number of pages | 60 |

Journal | Proceedings of Machine Learning Research |

Volume | 202 |

State | Published - 2023 |

Event | 40th International Conference on Machine Learning, ICML 2023 - Honolulu, United States Duration: Jul 23 2023 → Jul 29 2023 |

## ASJC Scopus subject areas

- Artificial Intelligence
- Software
- Control and Systems Engineering
- Statistics and Probability