Delayed Feedback in Kernel Bandits
Authors: Sattar Vakili, Danyal Ahmed, Alberto Bernacchia, Ciara Pike-Burke
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also validate our theoretical results with simulations. |
| Researcher Affiliation | Collaboration | 1Media Tek Research 2Imperial College London. |
| Pseudocode | Yes | Algorithm 1 Batch Pure Exploration with Delays (BPE-Delay) |
| Open Source Code | Yes | The code for these experiments is provided in a Git Hub repository.2 2https://github.com/svakili89/delayed kernel bandit |
| Open Datasets | No | The paper states: 'These functions are generated by fitting a kernel based model to points randomly generated from a multivariate Gaussian.' This indicates generated data for simulation, not a publicly available dataset with concrete access information for training. |
| Dataset Splits | No | The paper discusses simulation experiments using generated data rather than standard public datasets with predefined train/validation/test splits. It does not specify exact split percentages or sample counts for validation. |
| Hardware Specification | No | The paper does not specify any particular hardware used for running the experiments (e.g., specific GPU or CPU models, memory details, or cloud instance types). |
| Software Dependencies | No | The paper does not list specific version numbers for software dependencies (e.g., Python, PyTorch, or other libraries). |
| Experiment Setup | Yes | We use a SE kernel with a length scale parameter l = 0.8 for f1 and l = 1.0 for f2 in order to generate these objective functions. The learner can then choose from |X| = 2500 points over a uniform 50 50 grid. The sampling noise is zero mean Gaussian with standard deviation σ = 0.02. The stochastic delay in the feedback is generated from a Poisson distribution with parameter λ. The calculation of ψt for BPE-Delay uses ξ = 9 and b = 1 given in Assumption 4.2. |