Sketching for Convex and Nonconvex Regularized Least Squares with Sharp Guarantees
Authors: Yingzhen Yang, Ping Li
ICLR 2025 | Venue PDF | Archive PDF | Plain Text | 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) |