Covariate-dependent Graphical Model Estimation via Neural Networks with Statistical Guarantees
Authors: Jiahe Lin, Yikai Zhang, George Michailidis
TMLR 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The performance of the proposed method is evaluated on several synthetic data settings and benchmarked against existing approaches. The method is further illustrated on real datasets involving data from neuroscience and finance, respectively, and produces interpretable results. [...] 4 Synthetic Data Experiments [...] 5 Real Data Experiments |
| Researcher Affiliation | Collaboration | Yikai Zhang, Machine Learning Research, Morgan Stanley; George Michailidis, Department of Statistics and Data Science University of California, Los Angeles. E-mail: EMAIL. |
| Pseudocode | Yes | Exhibit 1: DNN-based Covariate-dependent Graphical Model (DNN-CGM) Learning Pipeline |
| Open Source Code | Yes | The code repository containing all the implementation is available at https://github.com/George Michailidis/covariate-dependent-graphical-model. |
| Open Datasets | Yes | We consider a dataset from the Human Connectome Project analyzed in Lee et al. (2023) comprising of resting-state f MRI scans for 549 subjects. [...] The S&P 100 Index constituent dataset can be collected from Yahoo!Finance, with the list of tickers corresponding to the constituents available through Wikipedia. |
| Dataset Splits | Yes | For all settings, we train the model with β( ) parameterized with an MLP8 on 10,000 samples, and evaluate it on a test set of size 1000; [...] Data is further split into train/val/test periods that respectively span 2001-2017, 2018-2019, 2020 onwards. |
| Hardware Specification | Yes | All experiments are done on a NVIDIA RTX A5000 GPU. |
| Software Dependencies | No | The paper mentions relying on 'R package huge' and 'Py Torch' but does not provide specific version numbers for these software components. For instance, it does not state 'PyTorch 1.9' or 'R package huge 1.2.3'. |
| Experiment Setup | Yes | Table 7: hyper-parameters for the MLPs and model training. hidden layer size/dropout learning rate scheduler type scheduler stepsize(milestones) / decay epochs. G1 [128, 64], [128] / 0.3 0.0005 Step LR 20/0.25 50. |