Mixture Proportion Estimation Beyond Irreducibility

Yilun Zhu, Aaron Fjeldsted, Darren Holland, George Landon, Azaree Lintereur, and Clayton Scott, ``Mixture Proportion Estimation Beyond Irreducibility,'' In Proceedings of the 40th International Conference on Machine Learning. PMLR, 2023. [Paper] [Poster] [Video]

To do

• release the code that could reproduce experimental result
• clear-up the implementation

2023-6-1: released the code (users needs to specify the parameters to match the test setup); further clean-up needed

This folder includes 4 baseline MPE algorithms: DPL, EN, KM, TIcE,
extracted from https://github.com/dimonenka/DEDPUL and https://web.eecs.umich.edu/~cscott/code.html#kmpe.
The Regrouping algorithm was extracted from https://openreview.net/forum?id=aYAA-XHKyk.

The Subsampling algorithm (SuMPE) was implemented directly in the experiments_xxx.py files.
We also re-implemented DPL, EN, KM that leverage histogram implementation
(can be found in experiments_nuclear_SuMPE.py and KMPE_discrete.py)

All the results shown in experiment section and appendix in the paper can be reproduced by running the experiments_xxx.py files. To be specific:

• Unfolding, Gamma Ray Spectra Data:
  • run experiments_nuclear_SuMPE.py
• Domain Adaptation, Synthetic Data:
  • run experiments_synth_SuMPE.py
  • specify scenario = 'Domain adaptation'
• Domain Adaptation, Benchmark Data:
  • run experiments_UCI_MNIST_SuMPE.py
  • specify scenario = 'Domain adaptation', data_mode = xxx
• Selected/Reported at Random, Benchmark Data:
  • run experiments_UCI_MNIST_SuMPE.py
  • specify scenario = 'Reported at random', data_mode = xxx
• Appendix, When Irreducibility Holds:
  • run experiments_synth_SuMPE.py
  • specify scenario = 'irreducible'

with some hyperparameter (like sample sizes) may need to change to match experimental section exactly.