Summary of Uplift Modeling Under Limited Supervision, by George Panagopoulos et al.
Uplift Modeling Under Limited Supervision
by George Panagopoulos, Daniele Malitesta, Fragkiskos D. Malliaros, Jun Pang
First submitted to arxiv on: 28 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Methodology (stat.ME)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 graph neural network for estimating causal effects in e-commerce reduces the required training set size by leveraging common graphs in e-commerce data. The approach views node regression as a restricted problem with a few labeled instances, and develops a two-model neural architecture inspired by previous causal effect estimators. The model incorporates varying message-passing layers to encode information. Additionally, an acquisition function is used to guide the creation of the training set in scenarios with extremely low experimental budgets. Experimental results on large-scale networks show a clear advantage over state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this research, scientists developed a new way to predict what would happen if they changed something in e-commerce data without actually doing it. This is important because changing things can be expensive and time-consuming. They used special computer models called graph neural networks that can learn from smaller amounts of training data than before. These models are good at understanding patterns in complex data like the relationships between different products or customers. The new approach works well even when there isn’t much information to learn from. |
Keywords
* Artificial intelligence * Graph neural network * Regression
