Sketching Method for Large Scale Combinatorial Inference
Authors: Wei Sun, Junwei Lu, Han Liu
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate our theory and method through both synthetic simulations and a real application in neuroscience. |
| Researcher Affiliation | Academia | Will Wei Sun Department of Management Science University of Miami wsun@bus.miami.eduJunwei Lu Department of Biostatistics Harvard University junweilu@hsph.harvard.eduHan Liu Department of Computer Science Northwestern University hanliu@northwestern.edu |
| Pseudocode | Yes | Algorithm 1 Fast Connectivity Test |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code or a link to a code repository for the methodology. |
| Open Datasets | Yes | We apply our sketching-based inferential methods to an Neuroimaging study conducted by [28]. |
| Dataset Splits | Yes | Throughout all our experiments, we tune λ in bθ1 via cross-validation and use the same λ for the rest bθj. |
| Hardware Specification | No | The paper discusses computational time but does not specify the hardware (e.g., CPU, GPU models) used for experiments. |
| Software Dependencies | No | The paper mentions algorithms and methods like node-wise regression, CLIME, graphical lasso, and Bron-Kerbosch, but does not specify any software names with version numbers for implementation. |
| Experiment Setup | Yes | Throughout all our experiments, we tune λ in bθ1 via cross-validation and use the same λ for the rest bθj. We then estimate s as bs = bθ1 0 and estimate ϵ as 2/(bs d). We use the theoretical rate for τ and set τ = 0.5 p log(d)/n. |