CF-OPT: Counterfactual Explanations for Structured Prediction
<|reference_start|>CF-OPT: Counterfactual Explanations for Structured Prediction: Optimization layers in deep neural networks have enjoyed a growing popularity in structured learning, improving the state of the art on a variety of applications. Yet, these pipelines lack interpretability since they are made of two opaque layers: a highly non-linear prediction model, such as a deep neural network, and an optimization layer, which is typically a complex black-box solver. Our goal is to improve the transparency of such methods by providing counterfactual explanations. We build upon variational autoencoders a principled way of obtaining counterfactuals: working in the latent space leads to a natural notion of plausibility of explanations. We finally introduce a variant of the classic loss for VAE training that improves their performance in our specific structured context. These provide the foundations of CF-OPT, a first-order optimization algorithm that can find counterfactual explanations for a broad class of structured learning architectures. Our numerical results show that both close and plausible explanations can be obtained for problems from the recent literature.<|reference_end|>
@article{vivier-ardisson2024cf-opt:,
    title={CF-OPT: Counterfactual Explanations for Structured Prediction},
    author={Germain Vivier-Ardisson, Alexandre Forel, Axel Parmentier, Thibaut
  Vidal},
    journal={arXiv preprint arXiv:2405.18293},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.18293},
    primaryClass={cs.LG}
}