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 [1].
Concentration in unbounded metric spaces and algorithmic stability
Authors: Aryeh Kontorovich
ICML 2014 | Venue PDF | LLM Run Details
| 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. |