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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
Concentration in unbounded metric spaces and algorithmic stability
Authors: Aryeh Kontorovich
ICML 2014 | Venue PDF | LLM Run Details | Input Tokens: 17,665 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,299 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 | We prove an extension of Mc Diarmid s inequality for metric spaces with unbounded diameter. To this end, we introduce the notion of the subgaussian diameter, which is a distributiondependent re๏ฌnement of the metric diameter. Our technique provides an alternative approach to that of Kutin and Niyogi s method of weakly difference-bounded functions, and yields nontrivial, dimension-free results in some interesting cases where the former does not. |
| Researcher Affiliation | Academia | Aryeh Kontorovich EMAIL Department of Computer Science, Ben-Gurion University, Beer Sheva 84105, ISRAEL |
| Pseudocode | No | The paper focuses on theoretical proofs and mathematical derivations and does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not mention any open-source code for the methodology described. |
| Open Datasets | No | This is a theoretical paper and does not describe experiments with specific datasets or mention their public availability for training. |
| Dataset Splits | No | This is a theoretical paper and does not describe experiments with specific dataset splits for training, validation, or testing. |
| Hardware Specification | No | This is a theoretical paper and does not describe any experimental setup or hardware used. |
| Software Dependencies | No | This is a theoretical paper and does not mention specific software dependencies with version numbers. |
| Experiment Setup | No | This is a theoretical paper and does not describe any experimental setup details such as hyperparameters or training configurations. |