Generalized Dantzig Selector: Application to the k-support norm
Authors: Soumyadeep Chatterjee, Sheng Chen, Arindam Banerjee
NeurIPS 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experimental results confirm our theoretical analysis. |
| Researcher Affiliation | Academia | Soumyadeep Chatterjee Sheng Chen Arindam Banerjee Dept. of Computer Science & Engg. University of Minnesota, Twin Cities {chatter,shengc,banerjee}@cs.umn.edu |
| Pseudocode | Yes | Algorithm 1 ADMM for Generalized Dantzig Selector Algorithm 2 Algorithm for computing prox ICλ ( ) of sp |
| Open Source Code | No | The paper does not provide any explicit links to open-source code or state that code is released. |
| Open Datasets | No | The paper uses synthetic data generated internally: 'Data generation We fixed p = 600, and θ = (10, . . . , 10 I , 10, . . . , 10 I , 10, . . . , 10 I , 0, 0, . . ., 0 I throughout the experiment, in which nonzero entries were divided equally into three groups. The design matrix X were generated from a normal distribution such that the entries in the same group have the same mean sampled from N(0, 1). X was normalized afterwards. The response vector y was given by y = Xθ + 0.01 N(0, 1).' |
| Dataset Splits | No | The paper describes synthetic data generation and varying sample sizes ('n') for experiments, but it does not specify explicit training, validation, or test dataset splits, nor does it mention cross-validation. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for experiments. |
| Software Dependencies | No | The paper states: 'All experiments are implemented in MATLAB.' However, it does not specify a version number for MATLAB or any other software dependencies with their versions. |
| Experiment Setup | Yes | Data generation We fixed p = 600, and θ = (10, . . . , 10 I , 10, . . . , 10 I , 10, . . . , 10 I , 0, 0, . . ., 0 I throughout the experiment, in which nonzero entries were divided equally into three groups. The design matrix X were generated from a normal distribution such that the entries in the same group have the same mean sampled from N(0, 1). X was normalized afterwards. The response vector y was given by y = Xθ + 0.01 N(0, 1). We fixed n = 400 to obtain the ROC plot for k = {1, 10, 50}. λp ranged from 10 2 to 103. For each k, we repeated the experiment 100 times. |