Multi-objective Bayesian optimisation with preferences over objectives
Authors: Majid Abdolshah, Alistair Shilton, Santu Rana, Sunil Gupta, Svetha Venkatesh
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | 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 Artiļ¬cial 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. |