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].
Corruption Robust Active Learning
Authors: Yifang Chen, Simon S. Du, Kevin G. Jamieson
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We conduct theoretical studies on streaming-based active learning for binary classification under unknown adversarial label corruptions. |
| Researcher Affiliation | Academia | Yifang Chen, Simon S. Du, Kevin Jamieson Paul G. Allen School of Computer Science & Engineering University of Washington, Seattle,WA EMAIL |
| Pseudocode | Yes | Algorithm 1 Robust CAL (modified the elimination condition) |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code for their proposed method or experiments. |
| Open Datasets | No | This is a theoretical paper that does not describe experiments run on specific datasets for training. It discusses theoretical concepts of samples and label complexity. |
| Dataset Splits | No | This is a theoretical paper and does not describe any dataset splits for experimental validation. |
| Hardware Specification | No | This is a theoretical paper and does not describe any hardware used for running experiments. |
| Software Dependencies | No | This is a theoretical paper and does not mention specific software dependencies with version numbers used for running experiments. |
| Experiment Setup | No | This is a theoretical paper and does not describe an experimental setup with hyperparameters or system-level training settings. |