NOSTRA: A noise-resilient and sparse data framework for trust region based multi objective Bayesian optimization

2025-08-22

AI Summary

Multi-objective Bayesian optimization (MOBO) suffers from limited performance in sparse, data-scarce, and experimentally noisy settings—particularly where repeated evaluations at identical inputs yield stochastic outputs—hindering efficient approximation of the Pareto front. To address this, we propose NOSTRA, a novel MOBO framework that: (i) incorporates experimental uncertainty priors directly into Gaussian process surrogate modeling for improved fidelity under noise; and (ii) introduces a noise-aware acquisition function coupled with a dynamic trust-region mechanism that adaptively shrinks the search space to concentrate exploration on high-quality solution regions. On low-budget, non-space-filling noisy benchmark functions, NOSTRA achieves significantly faster convergence and higher accuracy in the Pareto set compared to state-of-the-art methods. It more efficiently leverages scarce observations, making it especially suitable for resource-constrained real-world applications such as randomized clinical trials and molecular dynamics simulations.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Reasoning under Uncertainty: Stochastic Optimization
• Constraint Satisfaction and Optimization: Constraint Optimization

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Multi-objective Bayesian optimization (MOBO) struggles with sparse (non-space-filling), scarce (limited observations) datasets affected by experimental uncertainty, where identical inputs can yield varying outputs. These challenges are common in physical and simulation experiments (e.g., randomized medical trials and, molecular dynamics simulations) and are therefore incompatible with conventional MOBO methods. As a result, experimental resources are inefficiently allocated, leading to suboptimal designs. To address this challenge, we introduce NOSTRA (Noisy and Sparse Data Trust Region-based Optimization Algorithm), a novel sampling framework that integrates prior knowledge of experimental uncertainty to construct more accurate surrogate models while employing trust regions to focus sampling on promising areas of the design space. By strategically leveraging prior information and refining search regions, NOSTRA accelerates convergence to the Pareto frontier, enhances data efficiency, and improves solution quality. Through two test functions with varying levels of experimental uncertainty, we demonstrate that NOSTRA outperforms existing methods in handling noisy, sparse, and scarce data. Specifically, we illustrate that, NOSTRA effectively prioritizes regions where samples enhance the accuracy of the identified Pareto frontier, offering a resource-efficient algorithm that is practical in scenarios with limited experimental budgets while ensuring efficient performance.

Problem

• Handling noisy sparse data in multi-objective optimization
• Improving efficiency with limited experimental observations
• Accelerating convergence to accurate Pareto frontiers

Innovation

• Integrates prior knowledge of experimental uncertainty
• Employs trust regions to focus sampling
• Constructs more accurate surrogate models