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..
Convergence of Uncertainty Sampling for Active Learning
Authors: Anant Raj, Francis Bach
ICML 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we perform an experimental evaluation for our proposed uncertainty sampling based active learning algorithm. The experiments are performed on both synthetic as well as real world data. |
| Researcher Affiliation | Academia | 1 Inria, Ecole Normale Superieure, PSL Research University, Paris, France 2 Department of Electrical and Computer Engineering, Coordinated Science Laboratory University of Illinois at Urbana-Champaign, USA. Correspondence to: Anant Raj <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Uncertainty Sampling in Binary Classification Algorithm 2 Uncertainty Sampling in Multi-Class Classification |
| Open Source Code | No | No explicit statement or link is provided for open-source code for the methodology described in the paper. |
| Open Datasets | Yes | Normalized binary version of datasets are downloaded from manikvarma.org/code/LDKL/download.html. |
| Dataset Splits | No | The paper does not explicitly provide training/validation/test splits or percentages for any of the datasets. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory) used for running the experiments are mentioned. |
| Software Dependencies | No | The paper mentions using "randomized Fourier features" but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | No | The paper specifies parameters like 'n' and 'µ' for synthetic data, but does not provide a comprehensive list of hyperparameters, training configurations, or a dedicated experimental setup section with all necessary details. |