Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Learning Determinantal Point Processes with Moments and Cycles
Authors: John Urschel, Victor-Emmanuel Brunel, Ankur Moitra, Philippe Rigollet
ICML 2017 | Venue PDF | 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. |