Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Active Learning from Weak and Strong Labelers
Authors: Chicheng Zhang, Kamalika Chaudhuri
NeurIPS 2015 | Venue PDF | LLM Run Details | Input Tokens: 15,147 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,786 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We provide an active learning algorithm for this setting, establish its statistical consistency, and analyze its label complexity to characterize when it can provide label savings over using the strong labeler alone. |
| Researcher Affiliation | Academia | Chicheng Zhang UC San Diego EMAIL Kamalika Chaudhuri UC San Diego EMAIL |
| Pseudocode | Yes | Algorithm 1 Active Learning Algorithm from Weak and Strong Labelers, Algorithm 2 Training Algorithm for Difference Classi๏ฌer |
| Open Source Code | No | The paper does not contain any statement about releasing source code or provide any links to a code repository. |
| Open Datasets | No | The paper describes a theoretical framework using abstract data distributions (U and D) but does not refer to any specific publicly available datasets, provide access links, or formal citations for data used in experiments. |
| Dataset Splits | No | The paper is theoretical and does not describe empirical experiments, therefore it does not provide specific training/validation/test dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any specific hardware used for running experiments. |
| Software Dependencies | No | The paper is theoretical and does not mention any specific software dependencies with version numbers for experimental setup. |
| Experiment Setup | No | The paper is theoretical and focuses on algorithm design and theoretical analysis rather than empirical experimentation, and therefore does not provide specific experimental setup details such as hyperparameter values or training configurations. |