Pairwise Ranking with Gaussian Kernels
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Abstract (arXiv)
Regularized pairwise ranking with Gaussian kernels is one of the cutting-edge learning algorithms. Despite a wide range of applications, a rigorous theoretical demonstration still lacks to support the performance of such ranking estimators. This work aims to fill this gap by developing novel oracle inequalities for regularized pairwise ranking. With the help of these oracle inequalities, we derive fast learning rates of Gaussian ranking estimators under a general box-counting dimension assumption on the input domain combined with the noise conditions or the standard smoothness condition. Our theoretical analysis improves the existing estimates and shows that a low intrinsic dimension of input space can help the rates circumvent the curse of dimensionality.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 3 of its 52 references that are in the map. First posted on arXiv 6 Apr 2023.
Builds on
3 of 52 references.
References · 3
- 2018Adaptivity of deep ReLU network for learning in Besov and mixed smooth Besov spaces: optimal rate and curse of dimensionalitycited by 160
- 2020Estimation error analysis of deep learning on the regression problem on the variable exponent Besov spacecited by 13
- 2015Oracle inequalities for ranking and U-processes with Lasso penaltycited by 1
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Citing papers per year: 2023: 2.
2 papers.