Meta-VBO: Utilizing Prior Tasks in Optimizing Risk Measures with Gaussian Processes

Authors: Quoc Phong Nguyen, Bryan Kian Hsiang Low, Patrick Jaillet

ICLR 2024 | Conference PDF | Archive PDF | Plain Text | 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.