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..
Sketching for Convex and Nonconvex Regularized Least Squares with Sharp Guarantees
Authors: Yingzhen Yang, Ping Li
ICLR 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results validate the efficiency and effectiveness of both the SRO and Iterative SRO algorithms. |
| Researcher Affiliation | Collaboration | Yingzhen Yang School of Computing and Augmented Intelligence Arizona State University, Tempe, AZ 85281, USA EMAIL Ping Li Vec ML Inc., Bellevue, WA 98004, USA EMAIL |
| Pseudocode | Yes | Algorithm 1 Iterative SRO |
| Open Source Code | No | The paper describes algorithms (SRO, Iterative SRO) and their effectiveness but does not contain any explicit statement about making the source code publicly available or provide a link to a repository. |
| Open Datasets | Yes | Figure 6 and Figure 7 illustrate the accuracy (left) and NMI (right) of sketched Noisy SSC by SRO with respect to various choices of the regularization weight λ on the Extended Yale-B Dataset. |
| Dataset Splits | No | The paper describes generating synthetic data for some experiments (e.g., in Section C.1 and C.3) and mentions using the Extended Yale-B Dataset in Section C.4. However, it does not provide specific training/test/validation splits for any of the datasets used or generated. For the Extended Yale-B Dataset, it only mentions 'X is of size 1024 x 2414' without details on how it was split for experiments. |
| Hardware Specification | Yes | the running time is reported for γ = 3 on a CPU of Intel i5-11300H. |
| Software Dependencies | No | The paper mentions using Fast Iterative Shrinkage-Thresholding Algorithm (FISTA) and Proximal Gradient Descent (PGD) but does not provide specific version numbers for these or any other software libraries, frameworks, or programming languages used for implementation. |
| Experiment Setup | Yes | Let M be the maximum number of iterations for FISTA, and we set M = 10000 for SRO and set M = 2000 for Iterative SRO... the maximum iteration number N for Iterative SRO in Algorithm 1 is always not greater than 5. (Section 7.1) We set λ = p log d/n. (Section C.1) We set λ = 0.1 p s log d/n, sparsity s = 3 log d. (Section C.3) We set α = 10λ, the sketch size n = n/5. (Section C.5) |