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..
Robust Learning from Noisy Side-information by Semidefinite Programming
Authors: En-Liang Hu, Quanming Yao
IJCAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, empirical study shows that the new objective armed with proposed algorithm outperforms state-of-the-arts in terms of both speed and accuracy. |
| Researcher Affiliation | Collaboration | En-Liang Hu1 , Quanming Yao2,3 1Department of Mathematic, Yunnan Normal University 24Paradigm Inc 3Department of Computer Science and Engineering, Hong Kong University of Science and Technology EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 RSDP: Robust semi-definite programming by majorization-minimization. 1: Initialization: X1 = 0. 2: for k = 1, . . . , K do 3: Xk =arg min X H( X, Xk) via ADMM or APG; 4: update Xk+1 = Xk + Xk; 5: end for 6: return XK+1. |
| Open Source Code | No | Availability of codes and data sets are in Appendix.B. The provided text does not contain Appendix B or a direct link/statement about open-sourcing the code described in the paper. |
| Open Datasets | Yes | Experiments are performed on the adult data sets that has been commonly used as benchmark data about NPKL learning [Zhuang et al., 2011]. |
| Dataset Splits | Yes | We randomly sample 20% pairs from T for training, 20% for validation, and the rest for testing. |
| Hardware Specification | Yes | Finally, all algorithms are implemented in Matlab run on a PC with a 3.07GHz CPU and 24GB RAM. |
| Software Dependencies | No | The paper mentions 'Matlab' and various algorithms/packages (e.g., 'FW', 'L-BFGS', 'nm APG', 'SADMM', 'SDPNAL', 'SDPLR') but does not specify version numbers for any of them. |
| Experiment Setup | Yes | All algorithm is stopped when the relative change of objective values in successive iterations is smaller than 10 5 or when the number of iterations reaches 2000. As for the rank r of initial solution X, in Sections 4.1 and 4.2 we follow [Burer and Monteiro, 2003] and set its value to be the largest r satisfying r(r + 1) m, where m is the total number of observed data (i.e, m is the number of must-link and cannotlink pairs in Section 4.1, the number of given neighbor pairs in Section 4.2 respectively). In Section 4.3 we set r = 10. The tradeoff parameter γ is set to 0.01 as a default. We set γ = 10 to obtain sparse solution. |