Multi-Fidelity Active Learning with GFlowNets
Alex Hernandez-Garcia Nikita Saxena Moksh Jain Cheng-Hao Liu Yoshua Bengio
In the last decades, the capacity to generate large amounts of data in
science and engineering applications has been growing steadily. Meanwhile, the
progress in machine learning has turned it into 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, high-dimensional spaces, where querying a high fidelity, black-box
objective function is very expensive. Progress in machine learning methods that
can efficiently tackle such problems would help accelerate currently crucial
areas such as drug and materials discovery. In this paper, we propose the use
of GFlowNets for multi-fidelity active learning, where multiple approximations
of the black-box function are available at lower fidelity and cost. GFlowNets
are recently proposed methods for amortised probabilistic inference that have
proven efficient for exploring large, high-dimensional spaces and can hence be
practical in the multi-fidelity setting too. Here, we describe our algorithm
for multi-fidelity active learning with GFlowNets and evaluate its performance
in both well-studied synthetic tasks and practically relevant applications of
molecular discovery. Our results show that multi-fidelity active learning with
GFlowNets can efficiently leverage the availability of multiple oracles with
different costs and fidelities to accelerate scientific discovery and
engineering design.