Robust Structured Estimation with Single-Index Models
Authors: Sheng Chen, Arindam Banerjee
ICML 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results are provided to support our theoretical analyses. |
| Researcher Affiliation | Academia | 1Department of Computer Science & Engineering, University of Minnesota-Twin Cities, Minnesota, USA. Correspondence to: Sheng Chen <shengc@cs.umn.edu>, Arindam Banerjee <banerjee@cs.umn.edu>. |
| Pseudocode | No | The paper describes algorithms using mathematical equations and prose but does not contain structured pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access to source code, such as a specific repository link or an explicit code release statement. |
| Open Datasets | No | The paper states 'Essentially we generate our data (x, y) from y = f ( θ , x + ϵ)', indicating synthetic data generation rather than the use of a publicly available dataset with concrete access information. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, or detailed splitting methodology) for training, validation, or testing. It mentions 'The sample size n varies from 200 to 1000' but not how this data is split. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library or solver names with version numbers, needed to replicate the experiments. |
| Experiment Setup | Yes | In the experiment, we focus on model (3) with sparse θ . The problem dimension is fixed as p = 1000, and the sparsity of θ is set to 10. Essentially we generate our data (x, y) from y = f ( θ , x + ϵ) , where x N(0, I) and ϵ N(0, σ2). σ ranges from 0.3 to 1.5. We choose three monotonically increasing f, f(z) = 1/(1 + exp( z)) (which is bounded and Lipschitz), f(z) = z3 (which is unbounded and non-Lipschitz), and f(z) = log(1 + exp(z)) (which is unbounded but Lipschitz). The sample size n varies from 200 to 1000. We use the estimator (23) in Section 3. ... The number of its iterations is fixed to 100. The best tuning parameter of i SILO is obtained by grid search. |