Estimating parameters of determinantal point processes using an MM algorithm.
Provides examples of the paper published in TMLR:
Takahiro Kawashima, Hideitsu Hino, "Minorization-Maximization for Learning Determinantal Point Processes," Transactions on Machine Learning Research, November 2023.
Please set your working directory at scripts/.
All the program are implemented in Julia.
| Language | Recommended ver. |
|---|---|
Julia |
≥ 1.8.0 |
| Library | Recommended ver. |
|---|---|
Random |
|
LinearAlgebra |
|
SparseArrays |
|
Plots |
≥ 1.36.1 |
UnicodePlots |
≥ 3.3.1 |
StatsBase |
≥ 0.33.21 |
Distributions |
≥ 0.25.76 |
DataFrames |
≥ 1.4.2 |
DataFramesMeta |
≥ 0.13.0 |
Query |
≥ 1.0.0 |
JLD2 |
≥ 0.4.30 |
DeterminantalPointProcesses |
≥ 0.2.2 |
MatrixEquations |
≥ 2.2.2 |
CSV |
≥ 0.10.7 |
MAT |
≥ 0.10.3 |
StatsPlots |
≥ 0.15.5 |
> git clone https://github.com/ISMHinoLab/DPPMMEstimation.git && cd DPPMMEstimation
> julia
(@v1.8) pkg> activate .
Activating project at `/path/to/DPPMMEstimation`
(DPPMMEstimation) pkg> instantiate
# the mandatory packages will be installed
# you can check the environment by `pkg> status`| File | Description |
|---|---|
scripts/exec_toydata.jl |
Example on the toy data |
scripts/exec_nottingham.jl |
Example on the Nottingham dataset |
scripts/exec_amazon.jl |
Example on the Amazon Baby Registry Dataset |
scripts/aggr_results.jl |
Aggregate an experimental result into a DataFrame |
- Kawashima, T. and Hino, H. "Minorization-Maximization for Learning Determinantal Point Processes," Transactions on Machine Learning Research, November 2023.
- Mariet, Z. and Sra, S. "Fixed-point Algorithms for Learning Determinantal Point Processes," ICML2015.
- Gillenwater, J. A., Kulesza, A., Fox, E. and Taskar, B. "Expectation-Maximization for Learning Determinantal Point Processes," NeurIPS2014.
- Nottingham Music Database: https://abc.sourceforge.net/NMD/
jukedeck/nottingham-dataset: https://github.com/jukedeck/nottingham-dataset
This repository is released under the GNU GPLv3 license.