Summary of Variational Shapley Network: a Probabilistic Approach to Self-explaining Shapley Values with Uncertainty Quantification, by Mert Ketenci et al.
Variational Shapley Network: A Probabilistic Approach to Self-Explaining Shapley values with Uncertainty Quantification
by Mert Ketenci, Iñigo Urteaga, Victor Alfonso Rodriguez, Noémie Elhadad, Adler Perotte
First submitted to arxiv on: 6 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 A machine learning research paper introduces a new method for estimating Shapley values, which provide insights into model decision-making processes. The proposed approach simplifies the computation of Shapley values by requiring only a single forward pass, making it more efficient than existing methods. Additionally, the authors explore incorporating probabilistic frameworks to capture uncertainty in explanations. They evaluate their technique on simulated and real datasets, demonstrating robust predictive and explanatory performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way is found to understand how machine learning models make decisions. This method helps explain why models say what they do by using a special tool called Shapley values. The method is faster than others because it only needs one step to work out the answers. It also tries to account for uncertainty in explanations, which makes them more reliable. The new approach works well on made-up and real data. |
Keywords
* Artificial intelligence * Machine learning
