Summary of Contextual Bandits in Payment Processing: Non-uniform Exploration and Supervised Learning at Adyen, by Akhila Vangara et al.
Contextual Bandits in Payment Processing: Non-uniform Exploration and Supervised Learning at Adyen
by Akhila Vangara, Alex Egg
First submitted to arxiv on: 30 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Information Retrieval (cs.IR)
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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 approach combines non-uniform exploration with supervised learning using regression oracles, which significantly improves performance in a real-world industrial context. This analysis is conducted within Adyen’s large global payments processor framework under the Empirical Risk Minimization (ERM) framework. The results reveal that while the approach introduces challenges due to rigid algorithmic assumptions, it also exhibits an “oscillation effect” where fluctuations in performance occur across iterations. To address this issue, more adaptable algorithms are needed to leverage the benefits of regression oracles without compromising policy performance over time. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In a nutshell, researchers have developed a way to improve decision-making systems by combining two techniques: non-uniform exploration and supervised learning with regression oracles. This approach works well in real-world scenarios, but it also has some drawbacks, like making the system less stable over time. To make this method more reliable, scientists need to develop new algorithms that can adapt to changing conditions. |
Keywords
» Artificial intelligence » Regression » Supervised
