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..
Differentiable Expected Hypervolume Improvement for Parallel Multi-Objective Bayesian Optimization
Authors: Samuel Daulton, Maximilian Balandat, Eytan Bakshy
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our empirical evaluation demonstrates that q EHVI is computationally tractable in many practical scenarios and outperforms state-of-the-art multi-objective BO algorithms at a fraction of their wall time. |
| Researcher Affiliation | Industry | Samuel Daulton Facebook EMAIL Maximilian Balandat Facebook EMAIL Eytan Bakshy Facebook EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Acquisition functions are available as part of the open-source library Bo Torch [5]. Code is available at https://github.com/pytorch/botorch. |
| Open Datasets | Yes | For synthetic problems, we consider the Branin-Currin problem (d = 2, M = 2, convex Pareto front) [6] and the C2-DTLZ2 (d = 12, M = 2, V = 1, concave Pareto front), which is a standard constrained benchmark from the MO literature [16] |
| Dataset Splits | No | The paper does not specify training, validation, and test dataset splits in the conventional sense for supervised learning tasks. It describes evaluation budgets and number of trials for optimization problems. |
| Hardware Specification | Yes | Table 1: Acquisition Optimization wall time in seconds on a CPU (2x Intel Xeon E5-2680 v4 @ 2.40GHz) and a GPU (Tesla V100-SXM2-16GB). |
| Software Dependencies | No | While BoTorch is mentioned as an open-source library used, specific version numbers for software dependencies (e.g., BoTorch, PyTorch) are not provided in the paper's main text. |
| Experiment Setup | Yes | Both plots show optimization performance on a DTLZ2 problem (d = 6, M = 2) with a budget of 100 evaluations (plus the initial quasi-random design). We report means and 2 standard errors across 20 trials. |