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..
Gradient Hard Thresholding Pursuit for Sparsity-Constrained Optimization
Authors: Xiaotong Yuan, Ping Li, Tong Zhang
ICML 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical evidences show that our method is superior to the state-of-the-art greedy selection methods when applied to learning tasks of sparse logistic regression and sparse support vector machines. |
| Researcher Affiliation | Academia | Xiao-Tong Yuan EMAIL Department of Statistical Science, Cornell University, Ithaca, NY 14853, USA Dept. of Statistics & Biostatistics, Dept. of Computer Science, Rutgers University, Piscataway, NJ 08854, USA Ping Li EMAIL Dept. of Statistics & Biostatistics, Dept. of Computer Science, Rutgers University, Piscataway, NJ 08854, USA Tong Zhang EMAIL Dept. of Statistics & Biostatistics, Rutgers University, Piscataway, NJ 08854, USA |
| Pseudocode | Yes | Algorithm 1: Gradient Hard Thresholding Pursuit (Gra HTP). Initialization: x(0) with x(0) 0 k (typically x(0) = 0), t = 1. Output: x(t). repeat (S1) Compute x(t) = x(t 1) η f(x(t 1)); (S2) Let F (t) = supp( x(t), k) be the indices of x(t) with the largest k absolute values; (S3) Compute x(t) = arg min{f(x), supp(x) F (t)}; t = t + 1; until halting condition holds; Fast Gra HTP repeat Compute x(t) = x(t 1) η f(x(t 1)); Compute x(t) = x(t) k as the truncation of x(t) with top k (in magnitude) entries preserved; t = t + 1; until halting condition holds; |
| Open Source Code | No | The paper does not include an unambiguous statement that the authors are releasing the code for the work described, nor does it provide a direct link to a source-code repository. |
| Open Datasets | No | The paper mentions specific datasets 'rcv1.binary' and 'news20.binary' but does not provide a direct URL, DOI, specific repository name, or a formal citation with author names and year for public access to these datasets. |
| Dataset Splits | Yes | For rcv1.binary, a training subset of size 20,242 and a testing subset of size 20,000 are used. For news20.binary, a training subset of size 10,000 and a testing subset of size 9,996 are used. |
| Hardware Specification | Yes | All the considered algorithms are implemented in Matlab 7.12 running on a desktop with Intel Core i7 3.2G CPU and 16G RAM. |
| Software Dependencies | Yes | All the considered algorithms are implemented in Matlab 7.12 running on a desktop with Intel Core i7 3.2G CPU and 16G RAM. |
| Experiment Setup | Yes | We fix the regularization parameter λ = 10 4 in the objective of (6). ... We simply initialize w(0) = 0 and set the stopping criterion as w(t) w(t 1) / w(t 1) 10 4. |