Learning Determinantal Point Processes with Moments and Cycles
Authors: John Urschel, Victor-Emmanuel Brunel, Ankur Moitra, Philippe Rigollet
ICML 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we give experimental results that confirm our theoretical findings. |
| Researcher Affiliation | Academia | 1Department of Mathematics, MIT, USA. |
| Pseudocode | Yes | Algorithm 1 Compute Estimator ˆK |
| Open Source Code | No | The paper does not provide explicit statements or links for open-source code availability for the described methodology. |
| Open Datasets | No | We test our algorithm on two types of random matrices. First, we consider the matrix K RN N corresponding to the cycle on N vertices... Next, we consider the matrix K RN N corresponding to the clique on N vertices... The paper describes generating data randomly rather than using or providing access to a publicly available dataset. |
| Dataset Splits | No | The paper does not provide specific dataset split information for training, validation, or testing. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers. |
| Experiment Setup | No | The paper describes generating random instances for testing but does not provide specific experimental setup details such as hyperparameter values or training configurations for the algorithms. |