Robust Nonparametric Regression under Poisoning Attack

Authors: Puning Zhao, Zhiguo Wan

AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Experimental In this section we show some numerical experiments. In particular, we show the curve of the growth of mean square error over the attacked sample size q. More numerical results are shown in the full paper (Zhao and Wan 2023).
Researcher Affiliation Industry Zhejiang Lab Hangzhou, Zhejiang, China {pnzhao,wanzhiguo}@zhejianglab.com
Pseudocode No The computational complexity is higher than kernel regression up to a ln(M/ϵ) factor.
Open Source Code No More numerical results are shown in the full paper (Zhao and Wan 2023)... The detailed implementation and results are shown in Appendix I in the full paper (Zhao and Wan 2023).
Open Datasets No For each case, we generate N = 10000 training samples, with each sample follows uniform distribution in [0, 1]d. We have also conducted numerical experiments using real data... The detailed implementation and results are shown in Appendix I in the full paper (Zhao and Wan 2023).
Dataset Splits No For each case, we generate N = 10000 training samples, with each sample follows uniform distribution in [0, 1]d.
Hardware Specification No No specific hardware details for running the experiments are mentioned in the paper.
Software Dependencies No No specific software dependencies with version numbers are mentioned in the paper.
Experiment Setup Yes For the initial estimator (5), the parameters are T = 1 and M = 3. The corrected estimator is described in the full paper (Zhao and Wan 2023). For d = 1, the grid count is m = 50. For d = 2, m1 = m2 = 20. Consider that the optimal bandwidth (h in (5)) need to increase with the dimension, in (4), the bandwidths of all these four methods are set to be h = 0.03 for one dimensional distribution, and h = 0.1 for two dimensional case.