Local Differential Privacy for Bayesian Optimization
Authors: Xingyu Zhou, Jian Tan11152-11159
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We further conduct empirical comparisons of different algorithms over both synthetic and real-world datasets, which demonstrate the superior performance of our new Mo MA-GP-UCB algorithm in both private and non-private settings. |
| Researcher Affiliation | Collaboration | Xingyu Zhou,1 Jian Tan 2 1 ECE, The Ohio State University 2 Alibaba Group, Sunnyvale |
| Pseudocode | Yes | Algorithm 1 LDP-ATA-GP-UCB; Algorithm 2 LDP-TGP-UCB; Algorithm 3 Mo MA-GP-UCB |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository. |
| Open Datasets | Yes | Light sensor data. This data is collected in the CMU Intelligent Workplace in Nov 2005, which is available online as Matlab structure4; 4http://www.cs.cmu.edu/∼guestrin/Class/10708-F08/projects |
| Dataset Splits | No | The paper mentions "601 train samples and 192 test samples" for the Light sensor data but does not explicitly state the use of a validation set or specific train/validation/test splits for any of the datasets. |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python version, library versions). |
| Experiment Setup | Yes | The parameters used for each algorithm are set order-wise similar to those recommended by the theorems. We run each algorithm for 10 independent trials... The parameters for the kernel function are l = 0.2 for k SE and l = 0.2, ν = 2.5 for k Mat ern. We set B = maxx D |f(x)| and y(x) = f(x) + η. For the LDP case, the noise η is uniformly sampled in [−1, 1] and hence R = 1. For the non-private heavy-tailed case, the noise η are samples from the Student’s t-distribution with 3 degrees of freedom. Hence, v = B2 + 3 and c = 3. |