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].
Efficient Active Learning with Abstention
Authors: Yinglun Zhu, Robert Nowak
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our paper is theoretical in nature, and there is no negative societal impact of our work in the foreseeable future. |
| Researcher Affiliation | Academia | Yinglun Zhu Department of Computer Sciences University of Wisconsin-Madison Madison, WI 53706 EMAIL Robert Nowak Department of Electrical and Computer Engineering University of Wisconsin-Madison Madison, WI 53706 EMAIL |
| Pseudocode | Yes | Algorithm 1 Ef๏ฌcient Active Learning with Abstention |
| Open Source Code | No | The paper is theoretical and does not mention releasing any source code or provide links to a repository. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments on datasets, so there is no mention of public dataset availability. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments with dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe any experimental setup or the hardware used to run experiments. |
| Software Dependencies | No | The paper is theoretical and does not detail any software dependencies with specific version numbers for experimental reproducibility. |
| Experiment Setup | No | The paper is theoretical and focuses on algorithm design and proofs, not experimental setup details like hyperparameters or training configurations. |