Research map / Learning theory and algorithms / Prediction and decision losses
PRISM: A Rich Class of Parameterized Submodular Information Measures for Guided Data Subset Selection
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use MI functions that model both query relevance and diversity | Targeted subset selection · CIFAR-10, MNIST, SVHN, and P-MNIST | target-class accuracy: ≈ 20-30% gain; ≈ 12% higher than other methods | Helped |
| Use MI functions that model both query relevance and diversity | Targeted subset selection · CIFAR-10, MNIST, SVHN, and P-MNIST | overall accuracy: ≈ 2-10% | Helped |
| Replace MI with CMI functions (ablation) | Targeted subset selection · Targeted learning datasets | targeted and overall accuracy | No clear effect |
| Jointly learn internal parameters along with mixture weights (ablation) | Guided summarization mixture model · Image-collections dataset, all summarization flavors | V-ROUGE | Helped |
| Use MI functions that model both query relevance and diversity instead of relevance-focused or diversity-focused selection | Targeted subset selection · CIFAR-10, MNIST, SVHN, and P-MNIST | target-class accuracy | Helped |
Closest papers by meaning
- SELECT: A Large-Scale Benchmark of Data Curation Strategies for Image Classification (NeurIPS 2024 · similarity 0.85)
- The Best of Both Worlds: Bridging Quality and Diversity in Data Selection with Bipartite Graph (ICML 2025 · similarity 0.84)
- Most Influential Subset Selection: Challenges, Promises, and Beyond (NeurIPS 2024 · similarity 0.84)
- GIO: Gradient Information Optimization for Training Dataset Selection (ICLR 2024 · similarity 0.84)
- Multinoulli Extension: A Lossless Yet Effective Probabilistic Framework for Subset Selection over Partition Constraints (ICML 2025 · similarity 0.84)
- Less is More: Fewer Interpretable Region via Submodular Subset Selection (ICLR 2024 · similarity 0.84)
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