Proportionality in Ranking Compression
Motivated by applications in reinforcement learning from human feedback, we consider a setting where a given collection of rankings needs to be compressed into a smaller collection of rankings.
ProofPaper ↗
Key points
- This can also be seen as a combination of two fundamental social choice frameworks: ranking aggregation and multiwinner voting.
- We propose three proportionality notions that capture different types of representation in this setting: positional proportionality, pairwise proportionality, and proportionality for solid coalitions (PSC).
- On the one hand, we show that positional proportionality and PSC are always satisfiable for any input rankings and target compression size, and a desired output can be found in polynomial time.
- On the other hand, we prove that pairwise proportionality cannot be satisfied in general, but can nevertheless be attained when the input rankings are single-peaked or single-crossing.
Sources (1)
- [1]Proportionality in Ranking CompressionarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 16, 09:30 AM
Motivated by applications in reinforcement learning from human feedback, we consider a setting where a given collection of rankings needs to be compressed into a smaller collection of rankings.
This can also be seen as a combination of two fundamental social choice frameworks: ranking aggregation and multiwinner voting.
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