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..
Density Level Set Estimation on Manifolds with DBSCAN
Authors: Heinrich Jiang
ICML 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | The goal of this work is to give a theoretical analysis of the procedure and to the best of our knowledge, provide the ο¬rst analysis of density levelset estimation on manifolds. |
| Researcher Affiliation | Industry | 1Google. Correspondence to: Heinrich Jiang <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 DBSCAN False CC Removal |
| Open Source Code | No | The paper discusses DBSCAN, an existing procedure, and its theoretical analysis but does not provide concrete access to source code developed by the authors for the methodology described. |
| Open Datasets | No | This is a theoretical paper focused on analysis and proofs, not empirical evaluation. It does not mention any dataset used for training. |
| Dataset Splits | No | This is a theoretical paper focused on analysis and proofs, not empirical evaluation. It does not mention any dataset used for validation. |
| Hardware Specification | No | This is a theoretical paper and does not mention any specific hardware used for experiments. |
| Software Dependencies | No | This is a theoretical paper and does not specify any software dependencies with version numbers for replication. |
| Experiment Setup | No | This is a theoretical paper and does not provide specific experimental setup details such as hyperparameters or training configurations. |