Summary of Auto-bidding in Real-time Auctions Via Oracle Imitation Learning (oil), by Alberto Silvio Chiappa et al.
Auto-bidding in real-time auctions via Oracle Imitation Learning (OIL)
by Alberto Silvio Chiappa, Briti Gangopadhyay, Zhao Wang, Shingo Takamatsu
First submitted to arxiv on: 16 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 proposed framework aims to maximize acquisitions in multi-slot second-price auctions while adhering to budget and cost-per-acquisition constraints. This is achieved by framing the optimal bidding problem as a multiple-choice knapsack problem with a nonlinear objective, which is solved using an “oracle” algorithm that considers both past and future advertisement traffic data. The oracle solution serves as a training target for a student network that bids only based on real-time information, implemented through Oracle Imitation Learning (OIL). Numerical experiments demonstrate that OIL outperforms online and offline reinforcement learning algorithms, offering improved sample efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers has developed a new way to help online advertisers make the best decisions when buying ad space. This is done by using an “oracle” algorithm that looks at both past and future data about how people behave on the internet. The oracle solution helps train a student network to make good choices, even with limited information in real-time. This method is called Oracle Imitation Learning (OIL). It’s better than other methods because it can learn quickly and make smart decisions. |
Keywords
» Artificial intelligence » Reinforcement learning
