Black-Box Optimization From Small Offline Datasets via Meta Learning with Synthetic Tasks
Summary: arXiv:2604.12325v1 Announce Type: cross
Abstract: We consider the problem of offline black-box optimization, where the goal is to discover optimal designs (e.g., molecules or materials) from past experimental data. A key challenge in this setting is data scarcity: in many scientific applications, only small or poor-quality datasets are available, which severely limits the effectiveness of existing algorithms. Prior work has theoretically and empirically shown that performance of offline optimization algorithms depends on how well the surrogate model captures the optimization bias (i.e., ability to rank input designs correctly), which is challenging to accomplish with limited experimental data.
This paper proposes Surrogate Learning with Optimization Bias via Synthetic Task Generation (OptBias), a meta-learning framework that directly tackles data scarcity. OptBias learns a reusable optimization bias by training on synthetic tasks generated from a Gaussian process, and then fine-tunes the surrogate model on the small data for the target task.
Introduction to OptBias
The challenge of offline black-box optimization has gained significant attention in recent years, especially in fields such as materials science and drug discovery. The ability to find optimal designs despite limited data can lead to groundbreaking innovations. However, the traditional methods struggle with data scarcity, often resulting in suboptimal performance.
Key Features of OptBias
OptBias introduces several innovative features to overcome the challenges posed by limited datasets:
- Meta-Learning Approach: By utilizing a meta-learning framework, OptBias is able to adapt and generalize from synthetic tasks, which enables it to learn a reusable optimization bias.
- Synthetic Task Generation: The framework generates synthetic tasks using a Gaussian process, which allows for the exploration of a broader range of design spaces, thereby enhancing the training process.
- Fine-Tuning Capability: After acquiring the optimization bias, OptBias fine-tunes the surrogate model specifically for the target task using the limited available data, leading to improved performance in real-world applications.
Performance and Results
In extensive experiments conducted across various continuous and discrete offline optimization benchmarks, OptBias has demonstrated consistent superiority over state-of-the-art baselines, especially in scenarios characterized by small data regimes. The results indicate that:
- OptBias significantly enhances the surrogate model’s ability to rank input designs accurately, thereby improving the overall optimization outcomes.
- The framework exhibits robustness across different types of optimization tasks, confirming its applicability in diverse scientific fields.
- By addressing the fundamental issue of data scarcity, OptBias opens new avenues for research and practical applications in offline optimization.
Conclusion
In conclusion, the introduction of OptBias marks a significant advancement in the field of offline black-box optimization. By effectively utilizing meta-learning and synthetic task generation, this framework provides a practical and robust solution to the challenges posed by limited experimental data. As the demand for efficient design optimization continues to grow, OptBias stands out as a promising tool for researchers and practitioners alike, paving the way for future innovations in various scientific domains.