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 [1].
Improved algorithm and bounds for successive projection
Authors: Jiashun Jin, Tracy Ke, Gabriel Moryoussef, Jiajun Tang, Jingming Wang
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5 NUMERICAL STUDY We compare SPA, pp-SPA, and two simplified versions P-SPA and D-SPA (for illustration). ... We evaluate the vertex hunting error maxk{ Ëvk vk } (subject to a permutation of Ëv1, . . . , Ëv K). For each set of parameters, we report the average error over 20 repetitions. The results are in Figure 3. |
| Researcher Affiliation | Academia | Jiashun Jin & Gabriel Moryoussef Department of Statistics Carnegie Mellon University Pittsburgh, PA 15213, USA EMAIL Zheng Tracy Ke & Jiajun Tang & Jingming Wang Department of Statistics Harvard University Cambridge, MA 02138, USA EMAIL |
| Pseudocode | Yes | Algorithm 1 The (orthodox) Successive Projection Algorithm (SPA) ... Algorithm 2 Pseudo-Point Successive Projection Algorithm (pp-SPA) |
| Open Source Code | Yes | The code to reproduce these experiments is available at https://github.com/Gabriel78110/Vertex Hunting. |
| Open Datasets | No | The paper describes generating its own synthetic data for the experiments: 'Given (n, d, Ď), we first draw (n 30) points uniformly from the 2-dimensional simplex whose vertices are y1, y2, y3, and then put 10 points on each vertex of this simplex. ... Finally, we generate X1, X2, . . . , Xn from model (1).' No concrete access information (link, DOI, citation) is provided for a publicly available dataset. |
| Dataset Splits | No | The paper describes generating data for its experiments but does not provide details on specific training, validation, or testing splits (e.g., percentages, sample counts, or methods like cross-validation). |
| Hardware Specification | No | The paper does not provide any specific hardware details such as exact GPU/CPU models, processor types, or memory amounts used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., library or solver names with version numbers like Python 3.8, PyTorch 1.9) needed to replicate the experiment. |
| Experiment Setup | Yes | For pp-SPA and D-SPA, we need to specify tuning parameters (N, ). We use the heuristic choice in Remark 2. ... For N, we typically take N = log(n) in theory and N = 3 in practice. Concerning , we use a heuristic choice = maxi Yi Y /5, where Y = 1n Pn i=1 Yi. |