A Competitive Algorithm for Agnostic Active Learning
Authors: Yihan Zhou, Eric Price
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We take a different approach to agnostic active learning, getting an algorithm that is competitive with the optimal algorithm for any binary hypothesis class H and distribution DX over X. In particular, if any algorithm can use m queries to get O(η) error, then our algorithm uses O(m log |H|) queries to get O(η) error. Our main result is just such a competitive bound. |
| Researcher Affiliation | Academia | Eric Price Department of Computer Science University of Texas at Austin ecprice@cs.utexas.edu Yihan Zhou Department of Computer Science University of Texas at Austin joeyzhou@cs.utexas.edu |
| Pseudocode | Yes | Algorithm 1 Competitive Algorithm for Active Agnostic Learning |
| Open Source Code | No | No explicit statement about open-sourcing code or a link to a code repository is provided. |
| Open Datasets | No | The paper is theoretical and does not describe experiments using specific datasets, thus no information on public dataset availability for training is provided. |
| Dataset Splits | No | The paper is theoretical and does not describe experiments using specific datasets, thus no information on training/test/validation splits is provided. |
| Hardware Specification | No | The paper is theoretical and does not include details on specific hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not describe specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not provide details on experimental setup, hyperparameters, or training settings. |