Hypothesis Testing in Unsupervised Domain Adaptation with Applications in Alzheimer's Disease
Authors: Hao Zhou, Vamsi K. Ithapu, Sathya Narayanan Ravi, Vikas Singh, Grace Wahba, Sterling C. Johnson
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we present experiments on an AD study showing how CSF data from different batches (source/target) can be harmonized enabling the application of standard statistical analysis schemes. We performed evaluations on both synthetic data as well as data from an AD study. |
| Researcher Affiliation | Academia | William S. Middleton Memorial VA Hospital University of Wisconsin Madison |
| Pseudocode | No | The paper describes algorithmic steps and mathematical formulations but does not present them in a structured pseudocode block or explicitly labeled algorithm. |
| Open Source Code | No | The paper does not provide any specific links to open-source code repositories nor explicitly state that the code for the methodology is being released or made available. |
| Open Datasets | No | The paper uses “a dataset of individuals at risk for Alzheimer’s disease” and “CSF data from different batches” which are specific to their study, and does not provide concrete access information (link, DOI, formal citation) for public availability. |
| Dataset Splits | No | The paper discusses using source and target domains and “training” and “testing” samples, but does not specify explicit dataset splits (e.g., percentages or counts for training, validation, and test sets). |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU models, CPU types, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not list any specific software dependencies or libraries with their version numbers that would be needed to reproduce the experiments. |
| Experiment Setup | No | The paper describes the experimental design and the nature of the data used (synthetic and AD study data) but does not provide specific experimental setup details such as hyperparameter values, learning rates, batch sizes, or optimizer configurations. |