The Multiple Quantile Graphical Model
Authors: Alnur Ali, J. Zico Kolter, Ryan J. Tibshirani
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Lastly, we present detailed experiments that demonstrate the flexibility and effectiveness of the MQGM in modeling hetereoskedastic non-Gaussian data. (from Abstract) and Section 6 presents empirical examples comparing the MQGM versus common alternatives. (from Section 1 Introduction) |
| Researcher Affiliation | Academia | Alnur Ali Machine Learning Department Carnegie Mellon University alnurali@cmu.edu J. Zico Kolter Computer Science Department Carnegie Mellon University zkolter@cs.cmu.edu Ryan J. Tibshirani Department of Statistics Carnegie Mellon University ryantibs@cmu.edu |
| Pseudocode | No | The paper describes the computational approach using ADMM in Section 5, providing equations and a description of updates, but it does not include a structured pseudocode block or algorithm box. |
| Open Source Code | No | The paper does not include an explicit statement about releasing source code or provide a link to a code repository for the methodology described. |
| Open Datasets | Yes | We study n = 937 weekly flu incidence reports from September 28, 1997 through August 30, 2015, across 10 regions in the United States (see the top panel of Figure 2), obtained from [6]. [6] Centers for Disease Control and Prevention (CDC). Influenza national and regional level graphs and data, August 2015. URL http: //gis.cdc.gov/grasp/fluview/fluportaldashboard.html. |
| Dataset Splits | No | The paper mentions sample sizes for synthetic and flu data (e.g., 'n = 400 samples', 'n = 937 weekly flu incidence reports') but does not provide specific details on how these samples were split into training, validation, and test sets for reproducibility. |
| Hardware Specification | Yes | All subproblems took about 1 minute on a 6 core 3.3 Ghz Core i7 X980 processor. |
| Software Dependencies | No | The paper mentions software like ADMM and SCS for solving the problem but does not provide specific version numbers for these or other key software components used in the experiments. |
| Experiment Setup | Yes | First fix some ordered set A = {α1, . . . , αr} of quantile levels, e.g., A = {0.05, 0.10, . . . , 0.95}. For each variable yk, and each level αℓ, we model the conditional αℓ-quantile... Here λ1, λ2 0 are tuning parameters, Fℓkj, j = 1, . . . , d are univariate function spaces, ω > 0 is a fixed exponent... The MQGM was used with m = 10 basis functions (RBFs), and r = 20 quantile levels... We set m = 5, r = 99, and also introduced exogenous variables to encode the week numbers, so p = 1. |