Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Direct Estimation of Differential Functional Graphical Models
Authors: Boxin Zhao, Y. Samuel Wang, Mladen Kolar
NeurIPS 2019 | Venue PDF | 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 EMAIL Y. Samuel Wang Booth School of Business The Unveristy of Chicago Chicago, IL 60637 EMAIL Mladen Kolar Booth School of Business The Unveristy of Chicago Chicago, IL 60637 EMAIL |
| 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. |