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Pruning and fine-tuning for debiasing an already-trained neural network with applications to deep chest X-ray classifiers

Home Page: https://proceedings.mlr.press/v182/marcinkevics22a.html

Python 78.35% Shell 0.33% Jupyter Notebook 21.32%
classification debiasing debiasing-neural-networks fairness fairness-ml fine-tuning machine-learning neural-network pruning chest-xray-images

diff-bias-proxies's Introduction

👋 Hi! My name is Ričards Marcinkevičs. 🎓 Currently, I am a PhD student at the Institute for Machine Learning, Department of Computer Science, ETH Zurich. I am a Medical Data Science group member supervised by Prof. Dr. Julia E. Vogt and co-advised by Prof. Dr. Fanny Yang.

🤖 At the moment, I am broadly interested in interpretable and explainable machine learning. In particular, I would like to understand what are the inductive biases for neural networks that may render the model interpretable in specific use-cases and how such inductive biases may be incorporated into the model? Moreover, how can we leverage interpretations and explanations to obtain actionable insights about the data or the model itself, for instance, to perform scientific discovery or make our models fairer and more robust? From the application perspective, I work on time series and survival analysis and enjoy participating in interdisciplinary projects and leveraging ML methods to analyse biomedical data.

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diff-bias-proxies's Issues

TypeError: compute_accuracy_metrics() takes 2 positional arguments but 4 were given

Hello,

Congrats on your very interesting paper!

I was trying to run the code provided and I encountered the following error:

Traceback (most recent call last):
  File "main_ChestXRay.py", line 738, in <module>
    main(config)
  File "main_ChestXRay.py", line 107, in main
    model, train_acc, train_loss, val_acc, val_loss = train_ChestXRay_model(
  File "/diff-bias-proxies/models/networks_ChestXRay.py", line 348, in train_ChestXRay_model
    auroc, avg_precision, balanced_acc, f1_acc = compute_accuracy_metrics(probs_mat, preds_vec, labels_vec,
TypeError: compute_accuracy_metrics() takes 2 positional arguments but 4 were given

Indeed, the function compute_accuracy_metrics is defined with only two arguments in the file utils/evaluation.py.

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