Predictive Entropy Search for Multi-objective Bayesian Optimization
Authors: Daniel Hernandez-Lobato, Jose Hernandez-Lobato, Amar Shah, Ryan Adams
ICML 2016 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We compare PESMO with other methods on synthetic and real-world problems. The results show that PESMO produces better recommendations with a smaller number of evaluations, and that a decoupled evaluation can lead to improvements in performance, particularly when the number of objectives is large. |
| Researcher Affiliation | Collaboration | Daniel Hern andez-Lobato EMAIL Universidad Aut onoma de Madrid, Francisco Tom as y Valiente 11, 28049, Madrid, Spain. Jos e Miguel Hern andez-Lobato EMAIL Harvard University, 33 Oxford street, Cambridge, MA 02138, USA. Amar Shah EMAIL Cambridge University, Trumpington Street, Cambridge CB2 1PZ, United Kingdom. Ryan P. Adams EMAIL Harvard University and Twitter, 33 Oxford street Cambridge, MA 02138, USA. |
| Pseudocode | No | The paper describes the approach using mathematical equations and textual explanations but does not include any explicit pseudocode or algorithm blocks. |
| Open Source Code | No | We have coded all these methods in the software for Bayesian optimization Spearmint (https://github.com/HIPS/ Spearmint). |
| Open Datasets | Yes | We consider the MNIST dataset (Le Cun et al., 1998) |
| Dataset Splits | Yes | The prediction error is measured on a set of 10,000 instances extracted from the training set. The rest of the training data, i.e., 50,000 instances, is used for training. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions 'Keras library', 'Adam (D. Kingma, 2014)', and 'open-BLAS library' but does not specify version numbers for these software components. |
| Experiment Setup | Yes | The adjustable parameters are: The number of hidden units per layer (between 50 and 300), the number of layers (between 1 and 3), the learning rate, the amount of dropout, and the level of ℓ1 and ℓ2 regularization. |