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Leveraging Low-Rank Relations Between Surrogate Tasks in Structured Prediction

ICML 2019 · Learning theory and algorithms › Kernels and regression theory · approach: Statistical perspectives on generalization · 3 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Replace Hilbert-Schmidt regularization with trace-norm regularization (ablation)SELF · Movielens 100kpairwise ranking error: 0.312 ~(7 0.005) -> 0.156 ~(7 0.005)Helped
Replace Hilbert-Schmidt regularization with trace-norm regularization (ablation)SELF · Jester1pairwise ranking error: 0.386 ~(7 0.005) -> 0.247 ~(7 0.002)Helped
Replace Hilbert-Schmidt regularization with trace-norm regularization (ablation)SELF · Jester2pairwise ranking error: 0.366 ~(7 0.002) -> 0.340 ~(7 0.003)Helped
Replace Hilbert-Schmidt regularization with trace-norm regularization (ablation)SELF · Jester3pairwise ranking error: 0.375 ~(7 0.005) -> 0.343 ~(7 0.003)Helped
Replace Hilbert-Schmidt regularization with trace-norm regularization (ablation)SELF · Sushipairwise ranking error: 0.391 ~(7 0.003) -> 0.313 ~(7 0.003)Helped

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