@inproceedings{
bakker2023active,
title={Active Learning Policies for Solving Inverse Problems},
author={Tim Bakker and Thomas Hehn and Tribhuvanesh Orekondy and Arash Behboodi and Fabio Valerio Massoli},
booktitle={NeurIPS 2023 Workshop on Adaptive Experimental Design and Active Learning in the Real World},
year={2023},
url={https://openreview.net/forum?id=eLIk3m5C79}
}
In recent years, solving inverse problems for black-box simulators has become a point of focus for the machine learning community due to their ubiquity in science and engineering scenarios. In such settings, the simulator describes a forward process $f: (\psi, x) \rightarrow y$ from simulator parameters $\psi$ and input data $x$ to observations y, and the goal of the inverse problem is to optimise $\psi$ to minimise some observation loss. Simulator gradients are often unavailable or prohibitively expensive to obtain, making optimisation of these simulators particularly challenging. Moreover, in many applications, the goal is to solve a family of related inverse problems. Thus, starting optimisation ab-initio/from-scratch may be infeasible if the forward model is expensive to evaluate. In this paper, we propose a novel method for solving classes of similar inverse problems. We learn an active learning policy that guides the training of a surrogate and use the gradients of this surrogate to optimise the simulator parameters with gradient descent. After training the policy, downstream inverse problem optimisations require up to 90\% fewer forward model evaluations than the baseline.
Active Learning Policies for Solving Inverse Problems
Tim Bakker Thomas Hehn Tribhuvanesh Orekondy Arash Behboodi Fabio Valerio Massoli