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].
Local Search Algorithms for Rank-Constrained Convex Optimization
Authors: Kyriakos Axiotis, Maxim Sviridenko
ICLR 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the versatility of these methods on a wide range of applications involving matrix completion and robust principal component analysis. ... In Section 3, we evaluate the proposed greedy and local search algorithms on optimization problems like robust PCA. Then, in Section 4 we evaluate their generalization performance in machine learning problems like matrix completion. |
| Researcher Affiliation | Collaboration | Kyriakos Axiotis MIT EMAIL; Maxim Sviridenko Yahoo! Research NYC EMAIL |
| Pseudocode | Yes | Algorithm 1 Greedy; Algorithm 2 Local Search; Algorithm 3 Fast inner Optimization; Algorithm 4 Fast rank reduction; Algorithm 5 Fast Local Search |
| Open Source Code | No | The paper does not provide explicit links or statements regarding the open-sourcing of their developed code. It mentions implementations of other algorithms but not their own. |
| Open Datasets | Yes | In this section we compare our algorithms on the task of movie recommendation on the Movielens datasets Harper & Konstan (2015). |
| Dataset Splits | Yes | In order to evaluate the algorithms, we perform random 80%-20% train-test splits that are the same for all algorithms and measure the mean RMSE in the test set. |
| Hardware Specification | Yes | Code written in python and tested on an Intel Skylake CPU with 16 v CPUs. |
| Software Dependencies | No | The paper mentions "python" and specific algorithms like "LSQR algorithm" and "L-BFGS", but it does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | We solve the inner optimization problem by applying 10 L-BFGS iterations. ... We have implemented the Fast Greedy and Fast Local Search algorithms with 3 inner optimization iterations. ... For the inner optimization steps we have used the LSQR algorithm with 2 iterations in the 100K and 1M datasets, and with 3 iterations in the 10M dataset. ... We clip the entries of A between 1 and 5 when computing R(A) in Algorithm 2.3 and Algorithm 5. |