Direct Estimation of Differential Functional Graphical Models
Authors: Boxin Zhao, Y. Samuel Wang, Mladen Kolar
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We illustrate finite sample properties of our method through simulation studies. Finally, we apply our method to EEG data to uncover differences in functional brain connectivity between alcoholics and control subjects. |
| Researcher Affiliation | Academia | Boxin Zhao Department of Statistics The Unveristy of Chicago Chicago, IL 60637 boxinz@uchicago.edu Y. Samuel Wang Booth School of Business The Unveristy of Chicago Chicago, IL 60637 swang24@uchicago.edu Mladen Kolar Booth School of Business The Unveristy of Chicago Chicago, IL 60637 mkolar@chicagobooth.edu |
| Pseudocode | Yes | Algorithm 1 Functional differential graph estimation |
| Open Source Code | Yes | 1The code for this part is on https://github.com/boxinz17/Fu DGE |
| Open Datasets | Yes | We apply our method to electroencephalogram (EEG) data obtained from an alcoholism study [29, 6, 18] |
| Dataset Splits | Yes | Both M and L are chosen by 5-fold cross-validation as discussed in [18]. |
| Hardware Specification | No | No specific hardware details (e.g., GPU, CPU models, memory) were mentioned for running experiments. |
| Software Dependencies | No | The paper mentions functional data analysis techniques (e.g., FPCA, B-spline basis) and optimization methods (e.g., proximal gradient method, group lasso), but does not specify any software libraries or packages with version numbers used for implementation. |
| Experiment Setup | Yes | In each setting, we generate n X p functional variables from graph GX via Xij(t) = b(t)T δX ij , where b(t) is a five dimensional basis with disjoint support over [0, 1] with bk(t) = cos (10π (x (2k 1)/10)) + 1 (k 1)/5 x < k/5; 0 otherwise, k = 1, . . . , 5. ... We choose λn so that the estimated differential graph has approximately 1% of possible edges. The estimated edges of the differential graph are shown in Figure 3. |