Minimal Data, Maximum Clarity: A Heuristic for Explaining Optimization

• URL: http://arxiv.org/abs/2509.08667v1
• Date: Wed, 10 Sep 2025 15:03:53 GMT
• Title: Minimal Data, Maximum Clarity: A Heuristic for Explaining Optimization
• Abstract summary: This paper introduces EZR, a novel framework for multi-objective optimization that unifies active sampling, learning, and explanation.<n> EZR employs an active learning strategy based on Naive Bayes sampling to efficiently identify high-quality configurations.<n>It then distills optimization logic into concise decision trees, offering actionable explanations for both global and local decision-making.
• Score: 4.082216579462797
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Efficient, interpretable optimization is a critical but underexplored challenge in software engineering, where practitioners routinely face vast configuration spaces and costly, error-prone labeling processes. This paper introduces EZR, a novel and modular framework for multi-objective optimization that unifies active sampling, learning, and explanation within a single, lightweight pipeline. Departing from conventional wisdom, our Maximum Clarity Heuristic demonstrates that using less (but more informative) data can yield optimization models that are both effective and deeply understandable. EZR employs an active learning strategy based on Naive Bayes sampling to efficiently identify high-quality configurations with a fraction of the labels required by fully supervised approaches. It then distills optimization logic into concise decision trees, offering transparent, actionable explanations for both global and local decision-making. Extensive experiments across 60 real-world datasets establish that EZR reliably achieves over 90% of the best-known optimization performance in most cases, while providing clear, cohort-based rationales that surpass standard attribution-based explainable AI (XAI) methods (LIME, SHAP, BreakDown) in clarity and utility. These results endorse "less but better"; it is both possible and often preferable to use fewer (but more informative) examples to generate label-efficient optimization and explanations in software systems. To support transparency and reproducibility, all code and experimental materials are publicly available at https://github.com/amiiralii/Minimal-Data-Maximum-Clarity.

Related papers

• FrontierOR: Benchmarking LLMs' Capacity for Efficient Algorithm Design in Large-Scale Optimization [61.43300970020897]
  Large language models (LLMs) are increasingly used for optimization modeling and solver-code generation.<n>Existing benchmarks are limited to small or simplified examples far below real-world scale and complexity.<n>We introduce FrontierOR, among the first benchmarks to evaluate LLM-based efficient algorithm design for realistic large-scale optimization problems.
  arXiv  Detail & Related papers  (2026-05-24T20:10:42Z)
• Beyond Relational: Semantic-Aware Multi-Modal Analytics with LLM-Native Query Optimization [35.60979104539273]
  Nirvana is a multi-modal data analytics framework that incorporates programmable semantic operators.<n>Nirvana is able to reduce end-to-end runtime by 10%--85% and reduces system processing costs by 76% on average.
  arXiv  Detail & Related papers  (2025-11-25T01:41:49Z)
• Bayesian Optimization in Language Space: An Eval-Efficient AI Self-Improvement Framework [0.0]
  Large Language Models (LLMs) have recently enabled self-improving AI, i.e., AI that iteratively generates, evaluates, and refines its own outcomes.<n>In many societal applications, the primary limitation is not generating new solutions but evaluating them.<n>This paper overcomes this challenge by proving that the combination of the simple and widely used Best-of-N selection strategy and simple textual gradients statistically emulates the behavior of the gradients on the canonical UCB acquisition function.
  arXiv  Detail & Related papers  (2025-11-15T07:04:44Z)
• Pre-trained knowledge elevates large language models beyond traditional chemical reaction optimizers [0.0]
  We demonstrate that pre-trained knowledge in large language models (LLMs) fundamentally changes this paradigm.<n>LLM-GO excels precisely where traditional methods struggle: complex categorical spaces requiring domain understanding rather than mathematical optimization.
  arXiv  Detail & Related papers  (2025-08-27T21:09:51Z)
• On the Role of Feedback in Test-Time Scaling of Agentic AI Workflows [71.92083784393418]
  Agentic AI (systems that autonomously plan and act) are becoming widespread, yet their task success rate on complex tasks remains low.<n>Inference-time alignment relies on three components: sampling, evaluation, and feedback.<n>We introduce Iterative Agent Decoding (IAD), a procedure that repeatedly inserts feedback extracted from different forms of critiques.
  arXiv  Detail & Related papers  (2025-04-02T17:40:47Z)
• Make Optimization Once and for All with Fine-grained Guidance [78.14885351827232]
  Learning to Optimize (L2O) enhances optimization efficiency with integrated neural networks.<n>L2O paradigms achieve great outcomes, e.g., refitting, generating unseen solutions iteratively or directly.<n>Our analyses explore general framework for learning optimization, called Diff-L2O, focusing on augmenting solutions from a wider view.
  arXiv  Detail & Related papers  (2025-03-14T14:48:12Z)
• In-context Demonstration Matters: On Prompt Optimization for Pseudo-Supervision Refinement [71.60563181678323]
  Large language models (LLMs) have achieved great success across diverse tasks, and fine-tuning is sometimes needed to further enhance generation quality.<n>To handle these challenges, a direct solution is to generate high-confidence'' data from unsupervised downstream tasks.<n>We propose a novel approach, pseudo-supervised demonstrations aligned prompt optimization (PAPO) algorithm, which jointly refines both the prompt and the overall pseudo-supervision.
  arXiv  Detail & Related papers  (2024-10-04T03:39:28Z)
• OptiBench Meets ReSocratic: Measure and Improve LLMs for Optimization Modeling [62.19438812624467]
  Large language models (LLMs) have exhibited their problem-solving abilities in mathematical reasoning.<n>We propose OptiBench, a benchmark for End-to-end optimization problem-solving with human-readable inputs and outputs.
  arXiv  Detail & Related papers  (2024-07-13T13:27:57Z)
• Localized Zeroth-Order Prompt Optimization [54.964765668688806]
  We propose a novel algorithm, namely localized zeroth-order prompt optimization (ZOPO) ZOPO incorporates a Neural Tangent Kernel-based derived Gaussian process into standard zeroth-order optimization for an efficient search of well-performing local optima in prompt optimization. Remarkably, ZOPO outperforms existing baselines in terms of both the optimization performance and the query efficiency.
  arXiv  Detail & Related papers  (2024-03-05T14:18:15Z)
• Enhanced Bayesian Optimization via Preferential Modeling of Abstract Properties [49.351577714596544]
  We propose a human-AI collaborative Bayesian framework to incorporate expert preferences about unmeasured abstract properties into surrogate modeling. We provide an efficient strategy that can also handle any incorrect/misleading expert bias in preferential judgments.
  arXiv  Detail & Related papers  (2024-02-27T09:23:13Z)
• SEE: Strategic Exploration and Exploitation for Cohesive In-Context Prompt Optimization [8.975505323004427]
  We propose a novel Cohesive In-Context Prompt Optimization framework for Large Language Models (LLMs)<n>We introduce SEE, a scalable and efficient prompt optimization framework that adopts metaheuristic optimization principles and strategically exploration and exploitation.<n> SEE significantly outperforms state-of-the-art baseline methods by a large margin, achieving an average performance gain of 13.94 while reducing computational costs by 58.67.
  arXiv  Detail & Related papers  (2024-02-17T17:47:10Z)
• Functional Graphical Models: Structure Enables Offline Data-Driven Optimization [111.28605744661638]
  We show how structure can enable sample-efficient data-driven optimization. We also present a data-driven optimization algorithm that infers the FGM structure itself.
  arXiv  Detail & Related papers  (2024-01-08T22:33:14Z)
• Large-Batch, Iteration-Efficient Neural Bayesian Design Optimization [37.339567743948955]
  We present a novel Bayesian optimization framework specifically tailored to address the limitations of BO. Our key contribution is a highly scalable, sample-based acquisition function that performs a non-dominated sorting of objectives. We show that our acquisition function in combination with different Bayesian neural network surrogates is effective in data-intensive environments with a minimal number of iterations.
  arXiv  Detail & Related papers  (2023-06-01T19:10:57Z)
• Resource Aware Multifidelity Active Learning for Efficient Optimization [0.8717253904965373]
  This paper introduces the Resource Aware Active Learning (RAAL) strategy to accelerate the optimization of black box functions. The RAAL strategy optimally seeds multiple points at each allowing for a major speed up of the optimization task.
  arXiv  Detail & Related papers  (2020-07-09T10:01:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.