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Scalable Sampling for Nonsymmetric Determinantl Point Processes

Python Implmentation for Scalable Sampling for Nonsymmetric Determinantl Point Processes

  • The code files are organized for (1) sampling nonymmetric DPPs (NDPPs) and (2) learning with orthogonality constraints.
  • The code is based on https://github.com/cgartrel/nonsymmetric-DPP-learning/ (Scalable Learning and MAP Inference for Nonsymmetric Determinantal Point Processes, ICLR 2021)

Usage

Experiments for learning orthogonal NDPPs:

  • First, download the datasets

    bash download.sh
    
  • To run the ONDPP learn with UK Retail dataset,

    cd ./learning
    bash script_ondpp.sh
    

Experiments for scalable sampling from NDPPs:

  • To run synthetic dataset,

    cd ./sampling
    python run_synthetic.py
    
  • This will run the Cholesky-based sampling and tree-based rejection sampling

  • Parameters in ``run_synthetic.py'' (e.g., ground set size) can be changed

  • Models learned from real-world datasets also can be used for sampling (parameters are saved in ../learning/saved_models/)

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