Testing Determinantal Point Processes
Authors: Khashayar Gatmiry, Maryam Aliakbarpour, Stefanie Jegelka
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Small-scale experiments in Appendix J validate the algorithm empirically. |
| Researcher Affiliation | Academia | Khashayar Gatmiry MIT CSAIL gatmiry@mit.edu Maryam Aliakbarpour Univ. of Massachusetts Amherst maryam.aliakbarpour@gmail.com Stefanie Jegelka MIT CSAIL stefje@mit.edu |
| Pseudocode | Yes | Algorithm 1 DPP-Tester |
| Open Source Code | No | The paper does not contain an explicit statement about releasing its source code or a link to a code repository. |
| Open Datasets | No | The paper refers to receiving "samples from an unknown distribution q" for its testing algorithm, but does not explicitly mention using a specific named, publicly available dataset or provide access information for any dataset used in its experiments. |
| Dataset Splits | No | The paper does not explicitly provide information about training, validation, or test dataset splits. Its focus is on the sample complexity for distribution testing, rather than traditional model training with data splits. |
| Hardware Specification | No | The paper does not provide any specific details regarding the hardware used for running its experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers. |
| Experiment Setup | No | The paper does not provide specific details about the experimental setup, such as hyperparameter values, optimizer settings, or other system-level training configurations. |