We lifted 3 functions out of this paper's own repositories and ran 2 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| thomdeck/xpe | canonical | 2 of 3 |
| Function | Status | Where it lives |
|---|---|---|
| line_from_points | Ran | thomdeck/xpe/src/utils.py code served (permissive licence) · get_code("c329011944ef77b9") |
| zigzag | Ran | thomdeck/xpe/src/utils.py code served (permissive licence) · get_code("c03e099203209b39") |
| spatter | Not yet run | thomdeck/xpe/src/utils.py code served (permissive licence) · get_code("53773ea2ad271a59") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Monitoring and maintaining machine learning models are among the most critical challenges in translating recent advances in the field into real-world applications. However, current monitoring methods lack the capability of provide actionable insights answering the question of why the performance of a particular model really degraded. In this work, we propose a novel approach to explain the behavior of a black-box model under feature shifts by attributing an estimated performance change to interpretable input characteristics. We refer to our method that combines concepts from Optimal Transport and Shapley Values as Explanatory Performance Estimation (XPE). We analyze the underlying assumptions and demonstrate the superiority of our approach over several baselines on different data sets across various data modalities such as images, audio, and tabular data. We also indicate how the generated results can lead to valuable insights, enabling explanatory model monitoring by revealing potential root causes for model deterioration and guiding toward actionable countermeasures.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2408.13648")
get_code_for_paper("2408.13648")
have("2408.13648")
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