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..
Multi-objective Bayesian optimisation with preferences over objectives
Authors: Majid Abdolshah, Alistair Shilton, Santu Rana, Sunil Gupta, Svetha Venkatesh
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct a series of experiments to test the empirical performance of our proposed method MOBO-PC and compare with other strategies. These experiments including synthetic data as well as optimizing the hyper-parameters of a feed-forward neural network. For Gaussian process, we use maximum likelihood estimation for setting hyperparameters [21]. |
| Researcher Affiliation | Academia | Majid Abdolshah, Alistair Shilton, Santu Rana, Sunil Gupta, Svetha Venkatesh The Applied Arti๏ฌcial Intelligence Institute (A2I2), Deakin University, Australia {majid,alistair.shilton,santu.rana,sunil.gupta,svetha.venkatesh} @deakin.edu.au |
| Pseudocode | Yes | Algorithm 1 Test if v S I. Algorithm 2 Preference-Order Constrained Bayesian Optimisation (MOBO-PC). Algorithm 3 Calculate Pr(x XI|D). Algorithm 4 Calculate a PEHI t (x|D). |
| Open Source Code | No | The paper does not contain any statement about releasing source code or providing a link to a code repository. |
| Open Datasets | Yes | We are using MNIST dataset and the tuning parameters include number of hidden layers (x1 [1, 3]), the number of hidden units per layer (x2 [50, 300]), the learning rate (x3 (0, 0.2]), amount of dropout (x4 [0.4, 0.8]), and the level of l1 (x5 (0, 0.1]) and l2 (x6 (0, 0.1]) regularization. |
| Dataset Splits | No | The paper mentions using the MNIST dataset but does not explicitly state the training, validation, or test dataset splits or percentages. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions using Gaussian processes and discusses a neural network, but it does not provide specific version numbers for any software dependencies or libraries used in the experiments. |
| Experiment Setup | Yes | We are using MNIST dataset and the tuning parameters include number of hidden layers (x1 [1, 3]), the number of hidden units per layer (x2 [50, 300]), the learning rate (x3 (0, 0.2]), amount of dropout (x4 [0.4, 0.8]), and the level of l1 (x5 (0, 0.1]) and l2 (x6 (0, 0.1]) regularization. |