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..
Delayed Feedback in Kernel Bandits
Authors: Sattar Vakili, Danyal Ahmed, Alberto Bernacchia, Ciara Pike-Burke
ICML 2023 | Venue PDF | 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 Ο = 0.02. The stochastic delay in the feedback is generated from a Poisson distribution with parameter Ξ». The calculation of Οt for BPE-Delay uses ΞΎ = 9 and b = 1 given in Assumption 4.2. |