Robust Learning from Noisy Side-information by Semidefinite Programming
Authors: En-Liang Hu, Quanming Yao
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | 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 ynel.hu@gmail.com, yaoquanming@4paradigm.com |
| 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. |