Convergence of Uncertainty Sampling for Active Learning
Authors: Anant Raj, Francis Bach
ICML 2022 | Conference PDF | Archive PDF | Plain Text | 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 <anant.raj@inria.fr>. |
| 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. |