Robust Neural Contextual Bandit against Adversarial Corruptions
Authors: Yunzhe Qi, Yikun Ban, Arindam Banerjee, Jingrui He
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | 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}. |