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Code for Sample Efficient Learning of Predictors that Complement Humans (ICML 2022)

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Sample Efficient Learning of Predictors that Complement Humans

We provide two jupyter notebooks each replicates one of the figures in our paper:

  • joint_v_staged_model_complex.ipynb replicates Figure 2
  • joint_v_staged_sample_complexity.ipynb replicates Figure 3
  • python run_halfspaces.py replicates Figure 4

For reference implementation of learning to defer methods, please see our updated code in this repo instead https://github.com/clinicalml/human_ai_deferral

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Code for Sample Efficient Learning of Predictors that Complement Humans (ICML 2022)

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