Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Density Level Set Estimation on Manifolds with DBSCAN
Authors: Heinrich Jiang
ICML 2017 | Venue PDF | LLM Run Details | Input Tokens: 18,209 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 2,554 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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. |