The fast committor machine: Interpretable prediction with kernels
<|reference_start|>The fast committor machine: Interpretable prediction with kernels: In the study of stochastic systems, the committor function describes the probability that a system starting from an initial configuration $x$ will reach a set $B$ before a set $A$. This paper introduces an efficient and interpretable algorithm for approximating the committor, called the "fast committor machine" (FCM). The FCM uses simulated trajectory data to build a kernel-based model of the committor. The kernel function is constructed to emphasize low-dimensional subspaces that optimally describe the $A$ to $B$ transitions. The coefficients in the kernel model are determined using randomized linear algebra, leading to a runtime that scales linearly in the number of data points. In numerical experiments involving a triple-well potential and alanine dipeptide, the FCM yields higher accuracy and trains more quickly than a neural network with the same number of parameters. The FCM is also more interpretable than the neural net.<|reference_end|>
@article{aristoff2024the,
    title={The fast committor machine: Interpretable prediction with kernels},
    author={D. Aristoff, M. Johnson, G. Simpson, and R. J. Webber},
    journal={arXiv preprint arXiv:2405.10410},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.10410},
    primaryClass={math.NA cs.LG cs.NA stat.ML}
}