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..
Vector-Valued Control Variates
Authors: Zhuo Sun, Alessandro Barp, Francois-Xavier Briol
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate our methodology on a range of problems including multifidelity modelling, Bayesian inference for dynamical systems, and model evidence computation through thermodynamic integration. |
| Researcher Affiliation | Academia | 1University College London, London, UK 2University of Cambridge, Cambridge, UK 3The Alan Turing Institute, London, UK. |
| Pseudocode | Yes | Algorithm 1 Block-coordinate descent for vv-CVs with unknown task relationship |
| Open Source Code | Yes | The code to reproduce our results is available at: https://github.com/jz-fun/ Vector-valued-Control-Variates-Code. |
| Open Datasets | Yes | We use the dataset of snowshoe hares (preys) and Canadian lynxes (predators) from Hewitt (1921), and implement Bayesian inference on model parameters x by using no U-turn sampler (NUTS) in Stan (Carpenter et al., 2017). |
| Dataset Splits | No | The paper focuses on Monte Carlo methods and discusses sample sizes for integration tasks (e.g., 'm = (m L, m H) = (40, 40)'), but it does not specify explicit train/validation/test dataset splits with percentages or absolute counts for the input data itself. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU models (e.g., NVIDIA A100), CPU models (e.g., Intel Xeon), or cloud compute specifications used for running experiments. |
| Software Dependencies | No | The paper mentions using 'Stan (Carpenter et al., 2017)' and the 'Adam optimiser (Kingma & Ba, 2015)' but does not provide specific version numbers for these or any other software dependencies crucial for replication. |
| Experiment Setup | Yes | Hyper-parameter tuning: batch size 5; learning rate 0.05; total number of epochs 30. Base kernel: squared exponential kernel Optimisation: λ = 0.001; batch size is 5; learning rate is 0.001; total number of epochs 400. |