Prediction-Specific Design of Learning-Augmented Algorithms

• URL: http://arxiv.org/abs/2510.14887v1
• Date: Thu, 16 Oct 2025 17:06:53 GMT
• Title: Prediction-Specific Design of Learning-Augmented Algorithms
• Authors: Sizhe Li, Nicolas Christianson, Tongxin Li,
• Abstract summary: We show that prediction-specific performance criteria can enable significant performance improvements over coarser notions of consistency and robustness.<n>We develop a general bi-level optimization framework that enables systematically designing strongly-optimal algorithms.<n>Our results demonstrate that prediction-specific, strongly-optimal algorithms can significantly improve performance across a variety of online decision-making settings.
• Score: 10.918779264590297
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Algorithms with predictions} has emerged as a powerful framework to combine the robustness of traditional online algorithms with the data-driven performance benefits of machine-learned (ML) predictions. However, most existing approaches in this paradigm are overly conservative, {as they do not leverage problem structure to optimize performance in a prediction-specific manner}. In this paper, we show that such prediction-specific performance criteria can enable significant performance improvements over the coarser notions of consistency and robustness considered in prior work. Specifically, we propose a notion of \emph{strongly-optimal} algorithms with predictions, which obtain Pareto optimality not just in the worst-case tradeoff between robustness and consistency, but also in the prediction-specific tradeoff between these metrics. We develop a general bi-level optimization framework that enables systematically designing strongly-optimal algorithms in a wide variety of problem settings, and we propose explicit strongly-optimal algorithms for several classic online problems: deterministic and randomized ski rental, and one-max search. Our analysis reveals new structural insights into how predictions can be optimally integrated into online algorithms by leveraging a prediction-specific design. To validate the benefits of our proposed framework, we empirically evaluate our algorithms in case studies on problems including dynamic power management and volatility-based index trading. Our results demonstrate that prediction-specific, strongly-optimal algorithms can significantly improve performance across a variety of online decision-making settings.

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