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..
Noisy Dual Principal Component Pursuit
Authors: Tianyu Ding, Zhihui Zhu, Tianjiao Ding, Yunchen Yang, Rene Vidal, Manolis Tsakiris, Daniel Robinson
ICML 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | This paper extends the global optimality and convergence theory of DPCP to the case of data corrupted by noise, and further demonstrates its robustness using synthetic and real data. |
| Researcher Affiliation | Academia | 1Department of Applied Mathematics & Statistics, Johns Hopkins University, USA 2Mathematical Institute for Data Science, Johns Hopkins University, USA 3School of Information Science and Technology, Shanghai Tech University, China |
| Pseudocode | Yes | Algorithm 1 DPCP-PSGM for (3) |
| Open Source Code | No | The paper does not provide an explicit statement or link to open-source code for the described methodology. |
| Open Datasets | Yes | We use the 3D point clouds from the KITTI dataset (Geiger et al., 2013). In addition to the 7 frames annotated in Zhu et al. 2018a, we further annotate 131 frames. Each point cloud contains around 105 points with approximately 50% outliers. |
| Dataset Splits | No | The paper mentions tuning parameters on a 'randomly selected training set of 13 frames' and using 'the rest of the frames for evaluation', but does not explicitly define a separate validation set for hyperparameter tuning. |
| Hardware Specification | Yes | Experiments done on a laptop with Intel i7-6700HQ @ 2.6GHz CPU, 16GB 2133MHz DDR4 Memory. |
| Software Dependencies | No | The paper mentions parameters for algorithms (e.g., 'The λ of ℓ2,1RPCA is set to 1.92 M', 'µmin for DPCP-PSGD is set to 10 9'), but it does not specify software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | We tune the parameters of the algorithms on a randomly selected training set of 13 frames and use the rest of the frames for evaluation. Each method is tuned to achieve an optimal error and then re-tuned to be as fast as possible without exceeding 5% of that error. The λ of ℓ2,1RPCA is set to 1.92 M , the τ of DPCP-d is set to 2.76 N+M , µmin for DPCP-PSGD is set to 10 9, and the relative convergence accuracy, wherever applicable, is set to 10 6. |