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..
Meta-VBO: Utilizing Prior Tasks in Optimizing Risk Measures with Gaussian Processes
Authors: Quoc Phong Nguyen, Bryan Kian Hsiang Low, Patrick Jaillet
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We provide a theoretical performance guarantee for our algorithm and empirically demonstrate its performance using several synthetic function benchmarks and real-world objective functions. |
| Researcher Affiliation | Academia | Quoc Phong Nguyen1, Bryan Kian Hsiang Low2 & Patrick Jaillet1 1LIDS and EECS, Massachusetts Institute of Technology, USA 2School of Computing, National University of Singapore, Singapore |
| Pseudocode | No | The paper describes algorithms but does not provide structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper states 'provided the implementation of the experiments' but does not include an explicit statement about releasing the source code or a link to a repository. |
| Open Datasets | Yes | The experiments are performed on 6 synthetic functions: a Gaussian curve, the Branin-Hoo, the Goldstein-Price, the six-hump camel, the Hartmann-3D, and the Hartmann-6D (obtained from https://www.sfu.ca/~ssurjano); and using 2 real-world datasets: the yacht hydrodynamics dataset (Dua and Graff, 2017) and a portfolio optimization dataset (obtained from the existing works of Cakmak et al. (2020); Nguyen et al. (2021a)). and bibliography entry Dheeru Dua and Casey Graff. UCI machine learning repository, 2017. URL http://archive. ics.uci.edu/ml. |
| Dataset Splits | No | The paper mentions the datasets used but does not provide specific training/validation/test dataset splits, percentages, or absolute sample counts. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers, such as library names or solver versions. |
| Experiment Setup | Yes | As explained in Sec. 3.3, we set λ = 0 and η = 1. |