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].
Active Learning of Classifiers with Label and Seed Queries
Authors: Marco Bressan, Nicolò Cesa-Bianchi, Silvio Lattanzi, Andrea Paudice, Maximilian Thiessen
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This is a purely theoretical work with no direct societal impact, neither positive nor negative. |
| Researcher Affiliation | Collaboration | Marco Bressan Dept. of CS, Univ. of Milan, Italy EMAIL Nicolò Cesa-Bianchi DSRC & Dept. of CS, Univ. of Milan, Italy EMAIL Silvio Lattanzi Google EMAIL Andrea Paudice Dept. of CS, Univ. of Milan, Italy & Istituto Italiano di Tecnologia, Italy EMAIL Maximilian Thiessen Research Unit ML, TU Wien, Austria EMAIL |
| Pseudocode | Yes | Algorithm 1: Round(X, k) |
| Open Source Code | No | No statement or link providing concrete access to source code for the methodology described in the paper was found. The paper is purely theoretical. |
| Open Datasets | No | The paper is purely theoretical and does not involve experimental evaluation on datasets, thus no dataset access information is provided. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments, so no training, validation, or test dataset splits are provided. |
| Hardware Specification | No | The paper is purely theoretical and does not report on experiments, thus no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is purely theoretical and does not report on experiments, thus no specific software dependencies with version numbers are provided. |
| Experiment Setup | No | The paper is purely theoretical and does not report on experiments, thus no specific experimental setup details like hyperparameters are provided. |