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..
Global Optimization Networks
Authors: Sen Zhao, Erez Louidor, Maya Gupta
ICML 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments show the GON maximizers are statistically significantly better predictions than those produced by convex fits, GPR, or DNNs, and form more reasonable predictions for real-world problems. |
| Researcher Affiliation | Industry | 1Google Research, Mountain View, CA 94043 USA. Correspondence to: Sen Zhao <EMAIL>. |
| Pseudocode | No | The paper includes block diagrams and mathematical proofs, but no explicitly labeled "Pseudocode" or "Algorithm" blocks with structured steps. |
| Open Source Code | Yes | Code for some experiments can be found at https://github.com/google-research/googleresearch/tree/master/gon. |
| Open Datasets | Yes | The data can be downloaded at www.kaggle.com/senzhaogoogle/kingsreign. / publicly available at www.kaggle.com/senzhaogoogle/puzzlesales. / Using Kaggle data from Wine Enthusiast Magazine3...3www.kaggle.com/dbahri/wine-ratings / CIFAR10/100 (Krizhevsky, 2009), Fashion MNIST (Xiao et al., 2017), MNIST (Le Cun et al., 2010) and cropped SVHN (Netzer et al., 2011) datasets |
| Dataset Splits | Yes | For each experiment and for each method, we train a set of models with different hyperparameter choices, select the best model according to a validation or cross-validation metric (metric described below) / non-IID train/validation/test sets had 36/32/27 puzzles / 84,642 train samples, 12,092 validation samples, and 24,185 test samples, all IID. / default train/test splits, and use 10% of the train set as validation. |
| Hardware Specification | No | The paper mentions "our machines with 128GB of memory" but does not specify any particular GPU models, CPU models, or other detailed hardware components used for running experiments. |
| Software Dependencies | No | The paper mentions software like sklearn, TensorFlow, Keras, and ADAM, but it does not provide specific version numbers for these libraries or packages. |
| Experiment Setup | Yes | used ADAM (Kingma & Ba, 2015) with a default learning rate of .001 (preliminary experiments with learning rates of 0.0003 as suggested in Liu et al. (2020) yielded similar results). Batch size was N for N < 100, 1000 for the larger wine experiment in Sec 5.4, and 100 otherwise. / For GON and CGON, we use an ensemble of D unimodal lattices. All methods are trained for 250 epochs. / For both GON and CGON, we ο¬rst use D PLFs with K keypoints to calibrate the D inputs for optimization. The unimodal function consists of an enesemble of D unimodal lattices, each fuses 3 inputs with V keypoints. |