Active Learning for Network Estimation

We address the problem of estimating the structure of networks described as a system of differential equations. In each experiment, the network´s steady state is measured as an output depending on a controllable input. Due to the high cost of experiments, it is crucial to actively design the inputs for accurate estimation. Although standard active learning methods are designed to minimize the entropy of parameter distributions, it is very unstable to estimate the entropy of network structure. Therefore, we propose the two step algorithm as follows: first, the most uncertain link is chosen, and then the input is designed so as to minimize the variance of system equation parameter instead of network structure. Our method is tested in simulation experiments of gene networks following Yeung et al., PNAS (2002). We show that our algorithm gives stable and computationally effective solution.