Summary of A Multi-armed Bandit Approach to Online Selection and Evaluation Of Generative Models, by Xiaoyan Hu et al.
A Multi-Armed Bandit Approach to Online Selection and Evaluation of Generative Models
by Xiaoyan Hu, Ho-fung Leung, Farzan Farnia
First submitted to arxiv on: 11 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
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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 an online evaluation and selection framework to identify the best generative model among a group of available models, minimizing the costs of querying data from sub-optimal models. The framework views the task as a multi-armed bandit (MAB) problem and develops upper confidence bound (UCB) bandit algorithms for selecting the model producing data with the best evaluation score. Specifically, the paper presents MAB-based selection of generative models considering Fréchet Distance (FD) and Inception Score (IS) metrics, resulting in FD-UCB and IS-UCB algorithms. The paper proves regret bounds for these algorithms and presents numerical results on standard image datasets, suggesting the efficacy of MAB approaches for sample-efficient evaluation and selection of deep generative models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps us find the best generative model by trying different models and seeing which one produces the most useful data. We can use this method to choose a model that makes good images or writes nice stories. The researchers used special math to show that their way of choosing a model works well, even when we don’t have a lot of information about each model. |
Keywords
» Artificial intelligence » Generative model
