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..
Partial Hard Thresholding: Towards A Principled Analysis of Support Recovery
Authors: Jie Shen, Ping Li
NeurIPS 2017 | Venue PDF | 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 EMAIL; Ping Li Department of Statistics and Biostatistics Department of Computer Science Rutgers University New Jersey, USA EMAIL |
| 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. |