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[2602.03901] NeuroPareto: Calibrated Acquisition for Costly Many-Goal Search in Vast Parameter Spaces
Summary
NeuroPareto introduces a novel architecture for optimizing multi-objective problems in high-dimensional spaces, leveraging Bayesian classifiers and Gaussian processes to enhance efficiency and accuracy in costly evaluations.
Why It Matters
This research addresses a critical challenge in machine learning: optimizing multiple objectives under computational constraints. By improving the efficiency of search processes in vast parameter spaces, NeuroPareto has the potential to enhance various applications, from energy extraction to complex system design, making it a significant contribution to the field.
Key Takeaways
- NeuroPareto integrates advanced techniques for efficient multi-objective optimization.
- The architecture utilizes Bayesian classifiers to estimate uncertainty, improving candidate generation.
- Deep Gaussian Processes help separate predictive uncertainty, enhancing decision-making.
- The method maintains low computational overhead while ensuring high accuracy.
- Experiments demonstrate superior performance over existing optimization baselines.
Computer Science > Machine Learning arXiv:2602.03901 (cs) [Submitted on 3 Feb 2026 (v1), last revised 14 Feb 2026 (this version, v2)] Title:NeuroPareto: Calibrated Acquisition for Costly Many-Goal Search in Vast Parameter Spaces Authors:Rong Fu, Wenxin Zhang, Chunlei Meng, Youjin Wang, Haoyu Zhao, Jiaxuan Lu, Kun Liu, JiaBao Dou, Simon James Fong View a PDF of the paper titled NeuroPareto: Calibrated Acquisition for Costly Many-Goal Search in Vast Parameter Spaces, by Rong Fu and 8 other authors View PDF HTML (experimental) Abstract:The pursuit of optimal trade-offs in high-dimensional search spaces under stringent computational constraints poses a fundamental challenge for contemporary multi-objective optimization. We develop NeuroPareto, a cohesive architecture that integrates rank-centric filtering, uncertainty disentanglement, and history-conditioned acquisition strategies to navigate complex objective landscapes. A calibrated Bayesian classifier estimates epistemic uncertainty across non-domination tiers, enabling rapid generation of high-quality candidates with minimal evaluation cost. Deep Gaussian Process surrogates further separate predictive uncertainty into reducible and irreducible components, providing refined predictive means and risk-aware signals for downstream selection. A lightweight acquisition network, trained online from historical hypervolume improvements, guides expensive evaluations toward regions balancing convergence and diversity. With hierarchic...
