Summary of Adaptive Mixture Importance Sampling For Automated Ads Auction Tuning, by Yimeng Jia et al.
Adaptive Mixture Importance Sampling for Automated Ads Auction Tuning
by Yimeng Jia, Kaushal Paneri, Rong Huang, Kailash Singh Maurya, Pavan Mallapragada, Yifan Shi
First submitted to arxiv on: 20 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Applications (stat.AP)
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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 Adaptive Mixture Importance Sampling (AMIS), a novel method for optimizing key performance indicators (KPIs) in large-scale recommender systems like online ad auctions. Traditional importance sampling methods struggle with dynamic environments, particularly navigating complex multi-modal landscapes and avoiding local optima. The AMIS framework leverages a mixture distribution as the proposal distribution, dynamically adjusting both mixture parameters and mixing rates at each iteration to enhance search diversity and efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to make online recommendation systems better by using something called Adaptive Mixture Importance Sampling (AMIS). These systems help match people with things they might like, such as ads. The problem is that current methods struggle to keep up when the system changes or when there are many different options. AMIS tries to solve this by mixing together different ways of searching and adjusting how much each one contributes at each step. |
Keywords
» Artificial intelligence » Multi modal
