Inference and Sampling for Archimax Copulas
Authors: Yuting Ng, Ali Hasan, Vahid Tarokh
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experimentally compare to state-of-the-art density modeling techniques, and the results suggest that the proposed method effectively extrapolates to the tails while scaling to higher dimensional data. Our findings suggest that the proposed algorithms can be used in a variety of applications where understanding the interplay between the bulk and the tails of a distribution is necessary, such as healthcare and safety. |
| Researcher Affiliation | Academia | Yuting Ng Duke University yuting.ng@duke.edu Ali Hasan* Duke University ali.hasan@duke.edu Vahid Tarokh Duke University vahid.tarokh@duke.edu |
| Pseudocode | Yes | The full technique is presented in Algorithm 1 in Appendix A.1. ... The full technique is presented in Algorithm 2 in Appendix A.1. ... The full technique is presented in Algorithm 3 in Appendix A.1. |
| Open Source Code | Yes | The code for this paper is available at https://github.com/yutingng/gen-AX. |
| Open Datasets | Yes | Nutrient intake data [92], Copulas https://github.com/sdv-dev/Copulas, HAC toolbox https: //github.com/gorecki/HACopula, Py Torch, Sci Py, Num Py, Python. ... USDA. Dataset: CSFII 1985, continuing survey of food intakes by individuals, women 19-50 years of age and their children 1-5 years of age, 6 waves, 1985. Dataset, U.S. Department of Agriculture, Agriculture Research Service, 1985. https://www.ars.usda.gov/ARSUser Files/80400530/pdf/8586/ csfii85_6waves_doc.pdf, https://www.ars.usda.gov/northeast-area/ beltsville-md-bhnrc/beltsville-human-nutrition-research-center/ food-surveys-research-group/docs/csfii-1985-1986/. |
| Dataset Splits | No | The paper mentions in its checklist "Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [Yes] See Appendix B." but the provided text does not contain explicit details on train/validation/test splits (e.g., percentages or counts) or cross-validation setup. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware used for its experiments, such as GPU/CPU models, memory, or cloud instance types. |
| Software Dependencies | No | The paper lists "Py Torch, Sci Py, Num Py, Python" as existing assets but does not provide specific version numbers for these software dependencies, which would be necessary for reproducible replication. |
| Experiment Setup | Yes | Further experimental details may be found in Appendix B. ... Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [Yes] See Appendix B. |