Active clustering for labeling training data
Authors: Quentin Lutz, Elie de Panafieu, Maya Stein, Alex Scott
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In the first model, we characterize the algorithms that minimize the average number of queries required to cluster the items and analyze their complexity. In the second model, we analyze a specific algorithm family, propose as a conjecture that they reach the minimum average number of queries and compare their performance to a random approach. Our proofs, sketched in Section 3, rely on a broad variety of mathematical tools: probability theory, graph theory and analytic combinatorics. |
| Researcher Affiliation | Collaboration | Quentin Lutz Nokia Bell Labs quentin.lutz@nokia-bell-labs.com Élie de Panafieu Nokia Bell Labs elie.de_panafieu@nokia-bell-labs.com Alex Scott University of Oxford scott@maths.ox.ac.uk Maya Stein University of Chile mstein@dim.uchile.cl |
| Pseudocode | No | The paper describes algorithms verbally (e.g., 'The clique algorithm is an AC algorithm...', 'The universal algorithm finds the block...') but does not provide structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to open-source code for the methodology it describes. It only references a third-party software system. |
| Open Datasets | No | The paper is theoretical and analyzes algorithms based on random models for set partitions, rather than using or providing access to empirical datasets for training. |
| Dataset Splits | No | The paper does not describe empirical experiments or specific training/validation/test dataset splits. It focuses on theoretical analysis of algorithms. |
| Hardware Specification | No | The paper focuses on theoretical research and does not mention any specific hardware used for running experiments or computations. |
| Software Dependencies | Yes | [22] The Sage Developers. Sage Math, the Sage Mathematics Software System (Version 9.0). https://www.sagemath.org. 2020. |
| Experiment Setup | No | The paper focuses on theoretical analysis of algorithms and does not describe any empirical experiment setup details, such as hyperparameters or training configurations. |