Combinatorial semi-bandit with known covariance
Authors: Rémy Degenne, Vianney Perchet
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Figure 1: Left: parallel paths problem. Right: regret of OLS-UCB as a function of m and γ in the parallel paths problem with 5 paths (average over 1000 runs). |
| Researcher Affiliation | Collaboration | Rémy Degenne LMPA, Université Paris Diderot CMLA, ENS Paris-Saclay degenne@cmla.ens-cachan.fr Vianney Perchet CMLA, ENS Paris-Saclay CRITEO Research, Paris perchet@normalesup.org |
| Pseudocode | Yes | Algorithm 1 OLS-UCB. Require: Positive semi-definite matrix Γ, real parameter λ > 0. |
| Open Source Code | No | The paper does not contain any statement or link indicating that the source code for the methodology is openly available. |
| Open Datasets | No | The paper discusses theoretical bandit problems and simulations (e.g., 'parallel paths problem') but does not use a publicly available or open dataset with access information. |
| Dataset Splits | No | The paper does not specify training, validation, or test dataset splits. Its evaluation is based on theoretical analysis and simulations, not traditional dataset splits. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running experiments, such as CPU or GPU models. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., Python, PyTorch, or other libraries). |
| Experiment Setup | No | The paper mentions algorithm parameters like 'λ > 0' but does not provide specific hyperparameter values, training configurations, or system-level settings typically found in empirical experimental setups. |