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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Sketching Method for Large Scale Combinatorial Inference
Authors: Wei Sun, Junwei Lu, Han Liu
NeurIPS 2018 | Venue PDF | 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 EMAIL Lu Department of Biostatistics Harvard University EMAIL Liu Department of Computer Science Northwestern University EMAIL |
| 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. |