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..
Robust Neural Contextual Bandit against Adversarial Corruptions
Authors: Yunzhe Qi, Yikun Ban, Arindam Banerjee, Jingrui He
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also conduct experiments against baselines on real data sets under different scenarios, in order to demonstrate the effectiveness of our proposed R-Neural UCB. |
| Researcher Affiliation | Academia | University of Illinois at Urbana-Champaign Champaign, IL 61820 |
| Pseudocode | Yes | Algorithm 1 Robust Neural-UCB (R-Neural UCB) |
| Open Source Code | Yes | We include the source code along with our submission. |
| Open Datasets | Yes | Movie Lens 20M rating data set [41], Amazon Recommendation data set [43], MNIST data set [56] |
| Dataset Splits | No | The paper uses well-known datasets but does not explicitly state the training, validation, or testing splits (e.g., percentages or counts) or reference standard splits from citations. |
| Hardware Specification | Yes | All experiments are conducted on a server with an Intel Xeon CPU and NVIDIA V100 GPUs. |
| Software Dependencies | No | The paper mentions using deep learning models but does not provide specific software dependencies with version numbers (e.g., PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | For all UCB-based baselines, we choose the exploration parameter through grid search over the range {0.01, 0.1, 1}. We set L = 2 for all deep learning models, including our proposed Neural UCB-WGD and R-Neural UCB, and set the network width to m = 200. The learning rate for all neural algorithms is chosen by grid search from the range {0.0001, 0.001, 0.01}. For all methods, we select the regularization parameter λ from the range {0.0001, 0.001, 0.01}. The scaling parameter α for Neural UCB-WGD and R-Neural UCB is chosen from {0.2, 0.5, 1}. |