Multi-Fidelity Active Learning with GFlowNets
This addresses challenges in accelerating scientific and engineering areas like drug and materials discovery by improving data efficiency, though it is incremental as it builds on existing active learning and GFlowNet methods.
The paper tackles the problem of exploring large, high-dimensional spaces with expensive black-box functions in scientific discovery by proposing a multi-fidelity active learning algorithm with GFlowNets as a sampler, resulting in discovery of high-scoring candidates at a fraction of the budget of single-fidelity methods while maintaining diversity.
In the last decades, the capacity to generate large amounts of data in science and engineering applications has been growing steadily. Meanwhile, machine learning has progressed to become a suitable tool to process and utilise the available data. Nonetheless, many relevant scientific and engineering problems present challenges where current machine learning methods cannot yet efficiently leverage the available data and resources. For example, in scientific discovery, we are often faced with the problem of exploring very large, structured and high-dimensional spaces. Moreover, the high fidelity, black-box objective function is often very expensive to evaluate. Progress in machine learning methods that can efficiently tackle such challenges would help accelerate currently crucial areas such as drug and materials discovery. In this paper, we propose a multi-fidelity active learning algorithm with GFlowNets as a sampler, to efficiently discover diverse, high-scoring candidates where multiple approximations of the black-box function are available at lower fidelity and cost. Our evaluation on molecular discovery tasks shows that multi-fidelity active learning with GFlowNets can discover high-scoring candidates at a fraction of the budget of its single-fidelity counterpart while maintaining diversity, unlike RL-based alternatives. These results open new avenues for multi-fidelity active learning to accelerate scientific discovery and engineering design.