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..
Combinatorial semi-bandit with known covariance
Authors: Rémy Degenne, Vianney Perchet
NeurIPS 2016 | Venue PDF | 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 EMAIL Vianney Perchet CMLA, ENS Paris-Saclay CRITEO Research, Paris EMAIL |
| 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. |