Towards Safer Heuristics With XPlain
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Abstract (arXiv)
Many problems that cloud operators solve are computationally expensive, and operators often use heuristic algorithms (that are faster and scale better than optimal) to solve them more efficiently. Heuristic analyzers enable operators to find when and by how much their heuristics underperform. However, these tools do not provide enough detail for operators to mitigate the heuristic's impact in practice: they only discover a single input instance that causes the heuristic to underperform (and not the full set), and they do not explain why. We propose XPlain, a tool that extends these analyzers and helps operators understand when and why their heuristics underperform. We present promising initial results that show such an extension is viable.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 10 of its 52 references that are in the map. First posted on arXiv 19 Oct 2024.
Builds on
10 of 52 references.
References · 10
- 2017A Unified Approach to Interpreting Model Predictionscited by 6,488
- 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,040
- 2021Convex optimizationcited by 2,479
- 2017Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPRcited by 1,086
- 2023Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Modelscited by 550
- 2024LLMs Can't Plan, But Can Help Planning in LLM-Modulo Frameworkscited by 178
- 2024Beyond Functional Correctness: Exploring Hallucinations in LLM-Generated Codecited by 106
- 2023On the Viability of using LLMs for SW/HW Co-Design: An Example in Designing CiM DNN Acceleratorscited by 11
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- 2023Finding Adversarial Inputs for Heuristics using Multi-level Optimizationcited by 3
Built on by
Citing papers per year: 2025: 1; 2026: 1.
2 papers.