High Dimensional Bayesian Optimisation and Bandits via Additive Models
Authors: Kirthevasan Kandasamy, Jeff Schneider, Barnabas Poczos
ICML 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Via synthetic examples, a scientific simulation and a face detection problem we demonstrate that our method outperforms naive BO on additive functions and on several examples where the function is not additive. |
| Researcher Affiliation | Academia | Kirthevasan Kandasamy KANDASAMY@CS.CMU.EDU Jeff Schneider SCHNEIDE@CS.CMU.EDU Barnab as P oczos BAPOCZOS@CS.CMU.EDU Carnegie Mellon University, Pittsburgh, PA, USA |
| Pseudocode | Yes | Algorithm 1 Add-GP-UCB |
| Open Source Code | Yes | A Matlab implementation of our methods is available online at github.com/kirthevasank/add-gp-ucb. |
| Open Datasets | Yes | Here, we use galaxy data from the Sloan Digital Sky Survey to find the maximum likelihood values for 20 cosmological parameters. The likelihood is computed via an astrophysical simulation. Software is obtained from Tegmark et al (2006). ... In this experiment we use the VJ face dataset and the Open CV implementation (Bradski & Kaehler, 2008) which implements the classifier as a 22-stage cascade. |
| Dataset Splits | No | The paper initializes with Ninit points but does not specify explicit train/validation/test splits with percentages or sample counts for the evaluation datasets. The optimization process is sequential, where the "validation" is effectively part of the continuous evaluation of the acquisition function. |
| Hardware Specification | No | The paper does not provide specific hardware details such as CPU or GPU models used for running the experiments. |
| Software Dependencies | No | The paper mentions "Matlab implementation" and the "Di Rect" algorithm for maximizing the acquisition function, and "Open CV implementation" for face detection, but does not specify version numbers for these software components. |
| Experiment Setup | Yes | In our experiments, we set βt = 0.2 d log(2t) which offered a good tradeoff between exploration and exploitation. ... Following the recommendations in Bull (2011) we initialise Add-GP-UCB (and GP-UCB) using Ninit points selected uniformly at random. ... In our experiments, we choose the hyperparameters of the kernel by maximising the GP marginal likelihood (Rasmussen & Williams, 2006) every Ncyc iterations. |