Research map / Learning theory and algorithms / Kernels and regression theory
The Weighted Kendall and High-order Kernels for Permutations
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Average the top-k Kendall kernels instead of using standard Kendall | Kernel SVM · Eurobarometer 55.2 age-group classification | accuracy: 0.627 ± 0.050 -> 0.641 ± 0.058 | Helped |
| Learn weights via SUQUAN-SVD instead of using standard Kendall | Kernel SVM · Eurobarometer 55.2 age-group classification | accuracy: 0.627 ± 0.050 -> 0.635 ± 0.051 | Helped |
| Learn weights via alternative optimization instead of using standard Kendall | Kernel SVM · Eurobarometer 55.2 age-group classification | accuracy: 0.627 ± 0.050 -> 0.626 ± 0.051 | Hurt |
| Use top-4 weighting instead of standard Kendall (ablation) | Kernel SVM · Eurobarometer 55.2 age-group classification | accuracy: 0.627 ± 0.050 -> 0.632 ± 0.053 | No clear effect |
| Use top-5 weighting instead of standard Kendall (ablation) | Kernel SVM · Eurobarometer 55.2 age-group classification | accuracy: 0.627 ± 0.050 -> 0.631 ± 0.050 | No clear effect |
| Use top-3 weighting instead of standard Kendall (ablation) | Kernel SVM · Eurobarometer 55.2 age-group classification | accuracy: 0.627 ± 0.050 -> 0.624 ± 0.044 | No clear effect |
| Use top-2 weighting instead of standard Kendall (ablation) | Kernel SVM · Eurobarometer 55.2 age-group classification | accuracy: 0.627 ± 0.050 -> 0.590 ± 0.051 | Hurt |
| Use additive logarithmic rank weights instead of standard Kendall (ablation) | Kernel SVM · Eurobarometer 55.2 age-group classification | accuracy: 0.627 ± 0.050 -> 0.627 ± 0.050 | No clear effect |
| Use multiplicative hyperbolic rank weights instead of standard Kendall (ablation) | Kernel SVM · Eurobarometer 55.2 age-group classification | accuracy: 0.627 ± 0.050 -> 0.623 ± 0.048 | Hurt |
| Use multiplicative logarithmic rank weights instead of standard Kendall (ablation) | Kernel SVM · Eurobarometer 55.2 age-group classification | accuracy: 0.627 ± 0.050 -> 0.618 ± 0.048 | Hurt |
Closest papers by meaning
- Polynomial Time Learning Augmented Algorithms for NP-hard Permutation Problems (ICML 2025 · similarity 0.85)
- OT4P: Unlocking Effective Orthogonal Group Path for Permutation Relaxation (NeurIPS 2024 · similarity 0.84)
- REVISITING MULTI-PERMUTATION EQUIVARIANCE THROUGH THE LENS OF IRREDUCIBLE REPRESENTATIONS (ICLR 2025 · similarity 0.83)
- A Unified Analysis of Stochastic Gradient Descent with Arbitrary Data Permutations and Beyond (NeurIPS 2025 · similarity 0.82)
- Omnipresent Yet Overlooked: Heat Kernels in Combinatorial Bayesian Optimization (NeurIPS 2025 · similarity 0.82)
- Differentiable extensions with rounding guarantees for combinatorial optimization over permutations (NeurIPS 2025 · similarity 0.82)
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