Partial Hard Thresholding: Towards A Principled Analysis of Support Recovery
Authors: Jie Shen, Ping Li
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on the simulated data complement our theoretical findings and also illustrate the effectiveness of PHT. |
| Researcher Affiliation | Academia | Jie Shen Department of Computer Science School of Arts and Sciences Rutgers University New Jersey, USA js2007@rutgers.edu; Ping Li Department of Statistics and Biostatistics Department of Computer Science Rutgers University New Jersey, USA pingli@stat.rutgers.edu |
| Pseudocode | No | The paper describes the PHT(r) algorithm using mathematical equations (zt = xt 1 η F(xt 1), yt = PHTk zt; St 1, r , St = supp yt , xt = arg min x Rd F(x), s.t. supp (x) St.) but does not present them in a clearly labeled |
| Open Source Code | No | The paper does not contain any explicit statement about releasing source code or provide a link to a code repository. |
| Open Datasets | No | We consider the compressed sensing model y = A x+0.01e, where the dimension d = 200 and the entries of A and e are i.i.d. normal variables. Given a sparsity level s, we first uniformly choose the support of x, and assign values to the non-zeros with i.i.d. normals. |
| Dataset Splits | No | The paper describes the generation of simulated data for experiments but does not mention specific train/validation/test dataset splits from a pre-existing or publicly available dataset. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments. |
| Software Dependencies | No | The paper does not mention any specific software dependencies or their version numbers used for the experiments. |
| Experiment Setup | Yes | The step size η is fixed to be the unit, though one can tune it using cross-validation for better performance. |