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.
On Margin-Based Cluster Recovery with Oracle Queries
Authors: Marco Bressan, Nicolò Cesa-Bianchi, Silvio Lattanzi, Andrea Paudice
NeurIPS 2021 | Venue PDF | LLM Run Details | Input Tokens: 20,739 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 1,951 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 | This is a purely theoretical paper and does not imply any foreseeable societal consequence. |
| Researcher Affiliation | Collaboration | Marco Bressan Dept. of CS, Univ. of Milan, Italy Nicolò Cesa-Bianchi DSRC & Dept. of CS, Univ. of Milan, Italy Silvio Lattanzi Google Andrea Paudice Dept. of CS, Univ. of Milan, Italy & Istituto Italiano di Tecnologia, Italy |
| Pseudocode | No | The paper refers to algorithms like CHEATREC and MREC but does not provide pseudocode for them within the provided text. It mentions "The complete proof" can be found in supplementary material, but not pseudocode. |
| Open Source Code | No | If you ran experiments... Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [N/A] |
| Open Datasets | No | The paper is theoretical and does not conduct empirical studies using datasets. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments with data splits. |
| Hardware Specification | No | The paper is theoretical and does not conduct experiments requiring hardware specifications. The relevant section about experiments is marked as N/A. |
| Software Dependencies | No | The paper is theoretical and does not conduct experiments requiring software dependencies. The relevant section about experiments is marked as N/A. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or training settings. The relevant section about experiments is marked as N/A. |