Research map / Learning theory and algorithms / Submodular and game algorithms
Subset Selection and Summarization in Sequential Data
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| model transition dynamics in subset selection (SeqFL vs kDPP) | subset selection · Instructional videos, all tasks | F-score: 0.54 -> 0.65 | Helped |
| sequential diversity only (Seq-kDPP, M-kDPP) instead of kDPP | kDPP · Instructional videos, all tasks | F-score: 0.54 -> 0.57 | Helped |
| SeqFL vs Seq-kDPP on Make coffee | SeqFL · Instructional videos, Make coffee | F-score: 0.53 vs 0.59 | Hurt |
Closest papers by meaning
- Multinoulli Extension: A Lossless Yet Effective Probabilistic Framework for Subset Selection over Partition Constraints (ICML 2025 · similarity 0.84)
- Adaptive Data Optimization: Dynamic Sample Selection with Scaling Laws (ICLR 2025 · similarity 0.84)
- The Best of Both Worlds: Bridging Quality and Diversity in Data Selection with Bipartite Graph (ICML 2025 · similarity 0.84)
- Estimating Conditional Mutual Information for Dynamic Feature Selection (ICLR 2024 · similarity 0.84)
- Most Influential Subset Selection: Challenges, Promises, and Beyond (NeurIPS 2024 · similarity 0.83)
- Real-Time Selection Under General Constraints via Predictive Inference (NeurIPS 2024 · similarity 0.83)
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