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..
Bayesian Optimization with High-Dimensional Outputs
Authors: Wesley J. Maddox, Maximilian Balandat, Andrew G. Wilson, Eytan Bakshy
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate empirically how large-scale sampling from MTGPs can aid in challenging multi-objective, constrained, and contextual Bayesian Optimization problems (Section 4). |
| Researcher Affiliation | Collaboration | Wesley J. Maddox New York University EMAIL Maximilian Balandat Facebook EMAIL Andrew Gordon Wilson New York University EMAIL Eytan Bakshy Facebook EMAIL |
| Pseudocode | No | The paper describes methods and procedures in narrative text and mathematical equations, but does not include explicit pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is fully integrated into Bo Torch, see https://botorch.org/tutorials/composite_ bo_with_hogp and https://botorch.org/tutorials/composite_mtbo for tutorials. |
| Open Datasets | Yes | We consider a multi-task version of the Hartmann-6 function... We compare Matheron sampled MTGPs to batch independent MTGPs on the C2DTLZ2 [19]... and OSY [43]... Lunar Lander... from the Open AI Gym [8]... MOPTA08 benchmark problem [35]... Chemical Pollutants... originally deο¬ned in Bliznyuk et al. [5]... Optimizing PDEs... solved in py-pde [65]... Cell-Tower Coverage: Following Dreifuerst et al. [21]... Optical Interferometer... as in Sorokin et al. [53]. |
| Dataset Splits | No | The paper describes experiments on black-box optimization functions and simulated environments where data points are queried sequentially, rather than using pre-existing datasets with explicit train/validation/test splits. |
| Hardware Specification | Yes | on a single Tesla V100 GPU (a,b) and on a single CPU (c,d). |
| Software Dependencies | No | The paper mentions software like 'Bo Torch' and 'py-pde' but does not provide specific version numbers for these or other ancillary software components. |
| Experiment Setup | Yes | Following Daulton et al. [17] we use both q Par EGO and q EHVI with q = 2, for C2DTLZ2 and optimize for 200 iterations... using a batch size of q = 10, optimizing for 30 iterations... initialize with 150 data points and use Tu RBO with Thompson sampling with batches of q = 20 for a total of 1000 function evaluations and repeat over 30 trials. |