Summary of Neural Dueling Bandits, by Arun Verma et al.
Neural Dueling Bandits
by Arun Verma, Zhongxiang Dai, Xiaoqiang Lin, Patrick Jaillet, Bryan Kian Hsiang Low
First submitted to arxiv on: 24 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 The paper proposes new algorithms for the contextual dueling bandit problem, where the goal is to find the best arm for a given context using noisy preference feedback. The existing linear reward function assumption can be complex and non-linear in real-life applications like online recommendations or ranking web search results. To overcome this challenge, the authors use neural networks to estimate the reward function from preference feedback. They propose algorithms with sub-linear regret guarantees that efficiently select arms in each round. Experimental results on synthetic datasets corroborate the theoretical results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about a new way to solve a problem called contextual dueling bandit. Imagine you’re trying to find the best movie or song for someone, based on what they liked before. The challenge is that the reward function (how good the choice was) can be complicated and not linear. To fix this, researchers use a special kind of computer program (neural network) to estimate the reward function from feedback. They then developed algorithms that work well in real-life scenarios like online recommendations or search results. |
Keywords
» Artificial intelligence » Neural network
