Research map / Learning theory and algorithms / Prediction and decision losses
Structured Prediction with Projection Oracles
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use Birkhoff-polytope projection and decoding instead of squared loss with permutation decoding (ablation) | Label ranking · Label ranking datasets | Hamming loss: 5.70 -> 5.10 (Authorship); 7.11 -> 4.65 (Glass); 19.26 -> 2.96 (Iris); 9.04 -> 5.88 (Vehicle); 10.57 -> 8.76 (Vowel); 1. | Mixed |
| Use Birkhoff projection and decoding instead of unit cube projection and decoding (ablation) | Label ranking · Label ranking datasets | Hamming loss: 12.83 -> 5.10 (Authorship); 24.35 -> 4.65 (Glass); 27.78 -> 2.96 (Iris); 26.36 -> 5.88 (Vehicle); 43.71 -> 8.76 (Vowel) | Helped |
| Use unit cube or row-stochastic matrices instead of Birkhoff polytope for decoding (ablation) | Label ranking · Label ranking | Hamming loss | Hurt |
| Use Euclidean rather than KL projection (ablation) | Projection-based label-ranking losses · Label ranking | Hamming loss | No clear effect |
| Use order-simplex projection and decoding instead of ridge regression | Ordinal regression · 16 ordinal-regression datasets | Average MAE: 0.72 -> 0.43 | Helped |
| Add unit-cube projection to squared loss with order-simplex decoding (ablation) | Ordinal regression · 16 ordinal-regression datasets | Average MAE: 0.47 -> 0.45 | Helped |
| Replace unit-cube projection with order-simplex projection (ablation) | Ordinal regression · 16 ordinal-regression datasets | Average MAE: 0.45 -> 0.43 | Helped |
| Add order-simplex decoding to squared loss with rounding (ablation) | Ordinal regression · 16 ordinal-regression datasets | Average MAE: 0.72 -> 0.47 | Helped |
| Use knapsack-polytope projection and decoding instead of unit cube projection and decoding (ablation) | Multilabel classification · Birds | F1-score: 38.87 -> 39.43 | Helped |
| Use knapsack-polytope projection and decoding instead of unit cube projection and decoding (ablation) | Multilabel classification · Emotions | F1-score: 56.60 -> 62.57 | Helped |
| Use knapsack-polytope projection and decoding instead of unit cube projection and decoding (ablation) | Multilabel classification · Scene | F1-score: 61.06 -> 69.01 | Helped |
| Use knapsack-polytope projection and decoding instead of unit cube projection and decoding (ablation) | Multilabel classification · Cal500 | F1-score: 34.62 -> 34.61 | Hurt |
Closest papers by meaning
- On Structured Prediction Theory with Calibrated Convex Surrogate Losses (NeurIPS 2017 · similarity 0.89)
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- Trading off Consistency and Dimensionality of Convex Surrogates for Multiclass Classification (NeurIPS 2024 · similarity 0.85)
- Universal Online Convex Optimization with $1$ Projection per Round (NeurIPS 2024 · similarity 0.85)
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