Summary of Non-stationary Dueling Bandits Under a Weighted Borda Criterion, by Joe Suk and Arpit Agarwal
Non-Stationary Dueling Bandits Under a Weighted Borda Criterion
by Joe Suk, Arpit Agarwal
First submitted to arxiv on: 19 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes algorithms for K-armed dueling bandits that learn from preference feedback between arms. The key challenge is designing models with low dynamic regret, which measures the suboptimality of an arm compared to a winner arm. Recent studies have focused on non-stationary variants, where user preferences change over time (Saha and Gupta, 2022; Buening and Saha, 2023; Suk and Agarwal, 2023). The goal is to develop algorithms that adapt to changing preferences without knowing the extent of the changes ahead of time. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores ways for computers to learn from user feedback in situations where user preferences change over time. Imagine a music streaming service that needs to adjust its recommendations based on how users rate different songs. The goal is to develop algorithms that can adapt quickly to changing user preferences without knowing exactly when or why the changes will happen. |
