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..
Constrained Two-step Look-Ahead Bayesian Optimization
Authors: Yunxiang Zhang, Xiangyu Zhang, Peter Frazier
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In numerical experiments, 2-OPT-C typically improves query efficiency by 2x or more over previous methods, and in some cases by 10x or more. |
| Researcher Affiliation | Academia | Yunxiang Zhang Cornell University EMAIL Xiangyu Zhang Cornell University EMAIL Peter I. Frazier Cornell University EMAIL |
| Pseudocode | Yes | Pseudocode for using 2-OPT-C is provided in the supplement. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository for the methodology described. |
| Open Datasets | Yes | The benchmark problems include three synthetic problems from [1], named P1, P2, and P3, and two real-world problems, portfolio optimization and robot pushing. Detailed descriptions are in the supplement. |
| Dataset Splits | No | The paper describes sampling initial points and the number of function evaluations, but does not provide specific train/validation/test dataset splits for model training. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, memory amounts) used for running the experiments. |
| Software Dependencies | No | The paper mentions using 'GPy [38]' but does not provide a specific version number for this or any other software dependency. |
| Experiment Setup | Yes | The 2-OPT-C implementation uses GPs with a constant zero-mean prior and ARD square-exponential kernels for both objectives and constraints. GP hyperparameters are obtained by maximizing the marginal likelihood using GPy [38]. For the initialization of each experiment, we randomly sample three points with at least one feasible point from a Latin hypercube design. We run N = 40 function evaluations for P1 and P2, N = 60 for P3, N = 30 for portfolio optimization problem, and N = 50 for robot pushing problem. We use batch size of 1 for all five experiments. |