High-Dimensional Differential Parameter Inference in Exponential Family using Time Score Matching (AISTATS 2025)

Official Python implementation of the paper High-Dimensional Differential Parameter Inference in Exponential Family using Time Score Matching, published at AISTATS 2025.

In this paper, we introduce a method that tackle the differential parameter estimation in exponential family in a continuous setting. The main idea is treating the time score function of an exponential family model as a linear model of the differential parameter for direct estimation. We use time score matching to estimate parameter derivatives.

Prerequisites

Before you begin, ensure you have met the following requirements:

• You have installed the latest version of Conda.
• You have a Windows/Linux/Mac machine.

Installation

To install the necessary packages and set up the environment, follow these steps:

Clone the Repository

First, clone the repository to your local machine:

git clone https://github.com/Leyangw/tsm.git
cd tsm

Create the Conda Environment

We recommend creating a virtual environment with something such as anaconda, with Python version 3.11.4, e.g. in bash

conda create -n tsm python=3.11.4 ipython
conda activate tsm

and installing required packages given with the requirements.txt file

pip install -r requirements.txt

to ensure every package is installed correctly for this repo.

Current Folder

Code for reproducing SparTSM results:

• demo_fourier.ipynb: Figure 1
• demo_ROC.ipynb, demo_ROC_ratio.py: Figure 2
• demo_senate.ipynb: Figure 5

Requires python 3.10+ with torch, panda, networkx, and matplotlib packages.

Folder Structure

Folder for reproducing debiased estimator (SparTSM+) results (Figure 3, 4)

debiased/

Please see README file in the folder for detailed procedures for reproducing the results.

Folder for reproducing Loggle method reuslts

loggle/

Note that Loggle method was run and results were collected separately from the rest methods as it requires a special R environment.

Citation

If you find our paper, code, and/or data useful for your research, please cite our paper:

@inproceedings{williams2025high,
  title={High-Dimensional Differential Parameter Inference in Exponential Family using Time Score Matching},
  author={Williams, Daniel J and Wang, Leyang and Ying, Qizhen and Liu, Song and Kolar, Mladen},
  booktitle={International Conference on Artificial Intelligence and Statistics},
  year={2025}
}