Prediction-Specific Design of Learning-Augmented Algorithms

2025-10-16

AI Summary

Existing learning-augmented online algorithms are overly conservative and fail to exploit structural properties of prediction models. Method: This paper introduces the notion of “strong optimality,” which systematically optimizes performance under predictions while preserving robustness guarantees—marking the first integration of Pareto optimality into learning-augmented online algorithm design. We propose a bilevel optimization framework that unifies classical online algorithm design with data-driven techniques. Contribution/Results: We derive explicit deterministic and randomized strongly optimal algorithms for canonical problems—including ski-rental and maximum search—under both deterministic and stochastic prediction settings. Empirical evaluation on real-world applications—such as dynamic power management and volatility trading—demonstrates that our algorithms significantly outperform state-of-the-art learning-augmented approaches, thereby breaking the traditional consistency–robustness trade-off barrier.

Technology Category

• Search and Optimization: Learning to Search
• Machine Learning: Online Learning & Bandits
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

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.

Problem

• Optimizing prediction-specific performance in learning-augmented algorithms
• Achieving Pareto optimality beyond worst-case robustness and consistency
• Developing strongly-optimal algorithms for classic online problems

Innovation

• Bi-level optimization framework for strongly-optimal algorithms
• Prediction-specific design leveraging problem structure
• Pareto optimal tradeoffs between robustness and consistency