Robust Hypothesis Testing Using Wasserstein Uncertainty Sets
Authors: RUI GAO, Liyan Xie, Yao Xie, Huan Xu
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Real-data example using human activity data demonstrated the excellent performance of the new robust detector. [...] Section 5 demonstrates the excellent performance of our robust detectors using real-data for human activity detection. |
| Researcher Affiliation | Academia | Rui Gao School of Industrial and Systems Engineering Georgia Institute of Technology Atlanta, GA 30332 rgao32@gatech.edu Liyan Xie School of Industrial and Systems Engineering Georgia Institute of Technology Atlanta, GA 30332 lxie49@gatech.edu Yao Xie School of Industrial and Systems Engineering Georgia Institute of Technology Atlanta, GA 30332 yao.xie@isye.gatech.edu Huan Xu School of Industrial and Systems Engineering Georgia Institute of Technology Atlanta, GA 30332 huan.xu@isye.gatech.edu |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. It presents mathematical formulations and theoretical derivations. |
| Open Source Code | No | The paper does not provide any statements about releasing source code or links to a code repository. |
| Open Datasets | Yes | We adopt a dataset released by the Wireless Sensor Data Mining (WISDM) Lab in October 2013. The data in this set were collected with the Actitracker system, which is described in [19, 29, 15]. |
| Dataset Splits | No | The paper mentions using a dataset for 'human activity detection' and analyzes 'pre-change' and 'post-change' data for sequential observations, but it does not specify explicit training, validation, and test splits with percentages or sample counts. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU models, CPU types, or memory specifications used for running the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., Python 3.x, PyTorch 1.x). |
| Experiment Setup | Yes | We set θ1 = θ2 = θ as the sample sizes are identical, and θ is chosen such that the quantity 1 1/2 infφ Φ(φ; P 1 , P 2 ) in Table 1, or equivalently, the divergence between P 1 and P 2 , is close to zero with high probability if Q1 and Q2 are bootstrapped from the data before change, where P 1 , P 1 is the LFD yielding from (6). [...] For each sequence, we choose the threshold for detection by controlling the type-I error. |