Preferential Bayesian Optimization
Authors: Javier González, Zhenwen Dai, Andreas Damianou, Neil D. Lawrence
ICML 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We illustrate the benefits of PBO in a variety of experiments, showing that PBO needs drastically fewer comparisons for finding the optimum. |
| Researcher Affiliation | Collaboration | 1Amazon Research Cambridge, UK 2University of Sheffield, UK. |
| Pseudocode | Yes | Algorithm 1 The PBO algorithm. Input: Dataset D0 = {[xi, x i], yi}N i=1 and number of remaining evaluations n, acquisition for duels α([x, x ]). for j = 0 to n do 1. Fit a GP with kernel k to Dj and learn πf,j(x). 2. Compute the acquisition for duels α. 3. Next duel: [xj+1, x j+1] = arg max α([x, x ]). 4. Run the duel [xj+1, x j+1] and obtain yj+1. 5. Augment Dj+1 = {Dj ([xj+1, x j+1], yj+1)}. end for Fit a GP with kernel k to Dn. Returns: Report the current Condorcet s winner x n. |
| Open Source Code | No | The paper does not provide any statement or link indicating the availability of open-source code for the methodology described. |
| Open Datasets | Yes | The explicit formulation of these objectives and the domains in which they are optimized are available as part of standard optimization benchmarks3. https://www.sfu.ca/ssurjano/optimisation.html |
| Dataset Splits | No | The paper describes a sequential optimization process where data is collected through duels, rather than using traditional pre-defined training, validation, and test dataset splits for model evaluation. |
| Hardware Specification | No | The paper does not specify any particular hardware (e.g., GPU models, CPU types, or cloud instances with specs) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies or libraries used in the experiments. |
| Experiment Setup | Yes | The search of the optimum of the objectives is performed in a grid of size (33 per dimension for all cases), which has practical advantages: the integral in eq. (5) can easily be treated as a sum and, more importantly, we can compare PBO with bandit methods that are only defined in discrete domains. Each comparison starts with 5 initial (randomly selected) duels and a total budget of 200 duels are run, after which, the best location of the optimum should be reported. |