Research map / Learning theory and algorithms / Kernels and regression theory
Leveraging Low-Rank Relations Between Surrogate Tasks in Structured Prediction
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Replace Hilbert-Schmidt regularization with trace-norm regularization (ablation) | SELF · Movielens 100k | pairwise ranking error: 0.312 ~(7 0.005) -> 0.156 ~(7 0.005) | Helped |
| Replace Hilbert-Schmidt regularization with trace-norm regularization (ablation) | SELF · Jester1 | pairwise ranking error: 0.386 ~(7 0.005) -> 0.247 ~(7 0.002) | Helped |
| Replace Hilbert-Schmidt regularization with trace-norm regularization (ablation) | SELF · Jester2 | pairwise ranking error: 0.366 ~(7 0.002) -> 0.340 ~(7 0.003) | Helped |
| Replace Hilbert-Schmidt regularization with trace-norm regularization (ablation) | SELF · Jester3 | pairwise ranking error: 0.375 ~(7 0.005) -> 0.343 ~(7 0.003) | Helped |
| Replace Hilbert-Schmidt regularization with trace-norm regularization (ablation) | SELF · Sushi | pairwise ranking error: 0.391 ~(7 0.003) -> 0.313 ~(7 0.003) | Helped |
Closest papers by meaning
- A Consistent Regularization Approach for Structured Prediction (NeurIPS 2016 · similarity 0.91)
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- Incorporating Surrogate Gradient Norm to Improve Offline Optimization Techniques (NeurIPS 2024 · similarity 0.85)
- Trading off Consistency and Dimensionality of Convex Surrogates for Multiclass Classification (NeurIPS 2024 · similarity 0.85)
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