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..
Neural Active Learning Beyond Bandits
Authors: Yikun Ban, Ishika Agarwal, Ziwei Wu, Yada Zhu, Kommy Weldemariam, Hanghang Tong, Jingrui He
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We use extensive experiments to evaluate the proposed algorithms, which consistently outperform state-of-the-art baselines. |
| Researcher Affiliation | Collaboration | 1University of Illinois Urbana-Champaign, 2IBM Research |
| Pseudocode | Yes | Algorithm 1 NEURONAL-S and Algorithm 2 NEURONAL-P are included in the paper. |
| Open Source Code | No | The paper does not provide any explicit statement about releasing code or a link to a code repository for its methodology. |
| Open Datasets | Yes | We evaluate NEURONAL for both stream-based and pool-based settings on the following six public classification datasets: Adult, Covertype (CT), Magic Telescope (MT), Shuttle [24], Fashion [61], and Letter [18]. |
| Dataset Splits | Yes | The default label budget is 30% T. We perform hyperparameter tuning on the training set. |
| Hardware Specification | No | The paper describes neural network models and training, but it does not specify any hardware details like GPU models, CPU types, or memory used for the experiments. |
| Software Dependencies | No | The paper mentions using neural networks and activation functions (e.g., ReLU) but does not specify the versions of any software libraries, frameworks, or programming languages used (e.g., Python, PyTorch, TensorFlow). |
| Experiment Setup | Yes | For all NN models, we use the same width m = 100 and depth L = 2. We perform hyperparameter tuning on the training set. Each method has a couple of hyperparameters: the learning rate, number of epochs, batch size, label budget percentage, and threshold (if applicable). During hyperparameter tuning for all methods, we perform a grid search over the values {0.0001, 0.0005, 0.001} for the learning rate, {10, 20, 30, 40, 50, 60, 70, 80, 90} for the number of epochs, {32, 64, 128, 256} for the batch size, {0.1, 0.3, 0.5, 0.7, 0.9} for the label budget percentage and {1, 2, 3, 4, 5, 6, 7, 8, 9} for the threshold (exploration) parameter. |