Efficient Minimax Signal Detection on Graphs
Authors: Jing Qian, Venkatesh Saligrama
NeurIPS 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present several experiments to highlight key properties of LMIT and to compare LMIT against other state-of-art parametric and non-parametric tests on synthetic and real-world data. |
| Researcher Affiliation | Academia | Jing Qian Division of Systems Engineering Boston University Brookline, MA 02446 jingq@bu.edu Venkatesh Saligrama Department of Electrical and Computer Engineering Boston University Boston, MA 02215 srv@bu.edu |
| Pseudocode | No | The paper describes its methodology in narrative text and mathematical formulations but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any statement about releasing source code, nor does it provide a link to a code repository. |
| Open Datasets | Yes | In this part we compare LMIT against existing state-of-art approaches on a 300-node lattice, a 200node random geometric graph (RGG), and a real-world county map graph (129 nodes) (see Fig.3,4). Following [1] we adopt an elevated-rate independent Poisson model for the county map graph. |
| Dataset Splits | No | The paper mentions generating synthetic data ('synthetically simulate 100 null and 100 alternative hypothesis') and using real-world data, but it does not specify explicit training, validation, or test dataset splits. |
| Hardware Specification | No | The paper mentions scaling limits for SDP solvers ('scale up to n 1500 nodes for sparse graphs like lattice and n 300 nodes for dense graphs'), but it does not provide any specific hardware details such as GPU or CPU models, or memory specifications. |
| Software Dependencies | No | The paper mentions using 'standard SDP solvers' but does not specify the names or version numbers of any software dependencies used in the experiments. |
| Experiment Setup | Yes | On a 15 10 lattice we fix the size (17 nodes) and the signal strength μ|S| = 3, and consider three different shapes (see Fig. 1) for the alternative hypothesis. For each shape we synthetically simulate 100 null and 100 alternative hypothesis and plot AUC performance of LMIT as a function of γ. and In this part we compare LMIT against existing state-of-art approaches on a 300-node lattice, a 200node random geometric graph (RGG), and a real-world county map graph (129 nodes)... We compare LMIT against several other tests, including simulated annealing (SA) [4], rectangle test (Rect), nearest-ball test (NB), and two naive tests: maximum test (Max T) and average test (Avg T). |