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..
The Parallel Knowledge Gradient Method for Batch Bayesian Optimization
Authors: Jian Wu, Peter Frazier
NeurIPS 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In our experiments on both synthetic functions and tuning practical machine learning algorithms, q-KG consistently ο¬nds better function values than other parallel BO algorithms, such as parallel EI [2, 19, 25], batch UCB [5] and parallel UCB with exploration [3]. q-KG provides especially large value when function evaluations are noisy. |
| Researcher Affiliation | Academia | Jian Wu, Peter I. Frazier Cornell University Ithaca, NY, 14853 EMAIL |
| Pseudocode | Yes | Algorithm 1 The q-KG algorithm |
| Open Source Code | Yes | The code in this paper is available at https://github.com/wujian16/q KG. |
| Open Datasets | Yes | First, we tune logistic regression on the MNIST dataset... In the second experiment, we tune a CNN on CIFAR10 dataset. |
| Dataset Splits | Yes | We train logistic regression on a training set with 60000 instances with a given set of hyperparameters and test it on a test set with 10000 instances. ... We train the CNN on the 50000 training data with certain hyperparameters and test it on the test set with 10000 instances. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions software like 'C++', 'python interface', 'GP regression and GP hyperparameter fitting methods', 'Metrics Optimization Engine', 'Spearmint', and 'Gpoptimization', but it does not specify version numbers for these software components. |
| Experiment Setup | Yes | We set the batch size to q = 4. ... We initiate our algorithms by randomly sampling 2d + 2 points from a Latin hypercube design, where d is the dimension of the problem. ... We use a constant mean prior and the ARD Mat ern 5/2 kernel. ... We set M = 1000 to discretize the domain following the strategy in Section 5.3. ... We tune 4 hyperparameters: mini batch size from 10 to 2000, training iterations from 100 to 10000, the β2 regularization parameter from 0 to 1, and learning rate from 0 to 1. |