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].
On the Power and Limits of Distance-Based Learning
Authors: Periklis Papakonstantinou, Jia Xu, Guang Yang
ICML 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This work is theoretical. It deals with general multi-class and low-distortion learning questions. |
| Researcher Affiliation | Academia | Periklis A. Papakonstantinou EMAIL MSIS, Business School, Rutgers University, Piscataway, NJ 08853, USA Jia Xu EMAIL Department of Computer Science, Hunter College, CUNY, 695 Park Ave, New York, NY 10065, USA & The Graduate Center, CUNY, 365 5th Ave, New York, NY 10016, USA Guang Yang EMAIL Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, China & Aarhus University, Aaogade 34, DK 8200 Aarhus, Denmark |
| Pseudocode | No | No pseudocode or algorithm blocks are present in the paper. |
| Open Source Code | No | The paper does not provide any statement or link indicating the release of open-source code for the described methodology. |
| Open Datasets | No | This work is theoretical and does not use or describe a dataset in the context of training or evaluating models. |
| Dataset Splits | No | This is a theoretical paper and does not involve empirical experiments with dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any hardware specifications for running experiments. |
| Software Dependencies | No | The paper is theoretical and does not list any software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or training configurations. |