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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
A Competitive Algorithm for Agnostic Active Learning
Authors: Yihan Zhou, Eric Price
NeurIPS 2023 | Venue PDF | 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 EMAIL Yihan Zhou Department of Computer Science University of Texas at Austin EMAIL |
| 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. |