Projective Preferential Bayesian Optimization
Authors: Petrus Mikkola, Milica Todorović, Jari Järvi, Patrick Rinke, Samuel Kaski
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section we demonstrate the effciency of the PPBO method in high-dimensional spaces, and experiment with various acquisition strategies in numerical experiments on simulated functions. (Section 4); In this section we demonstrate the capability of PPBO to correctly and effciently encode user preferences from pro jective preferential feedback. We consider a material science problem... (Section 5) |
| Researcher Affiliation | Academia | 1Helsinki Institute for Information Technology HIIT, De partment of Computer Science, Aalto University, Espoo, Fin land 2Department of Applied Physics, Aalto University, Espoo, Finland 3The University of Manchester, UK. |
| Pseudocode | Yes | Algorithm 1 Approximate EIn(ξ, x) |
| Open Source Code | Yes | Source code is available at https://github.com/Aalto PML/PPBO. |
| Open Datasets | Yes | For f we consider four different test functions: Six-hump camel2D, Hartmann6D, Levy10D and Ackley20D.3 (Footnote 3 points to 'https://www.sfu.ca/~ssurjano/optimization.html' which makes these standard benchmark functions openly accessible.) |
| Dataset Splits | No | The paper uses benchmark test functions and a user experiment, but does not provide specific details on train/validation/test dataset splits with percentages or counts for reproducibility. |
| Hardware Specification | Yes | All experiments of each test function were run on a computing infrastructure of 24x Xeon Gold 6148 2.40GHz cores and 72GB RAM. (Footnote 4, Section 4) |
| Software Dependencies | No | The paper mentions 'quantum mechanical atomistic simulation code FHI-aims (Blum et al., 2009)' but does not provide specific version numbers for it or any other software dependencies needed to replicate the experiment. |
| Experiment Setup | Yes | We consider a total budget of 100 queries. The ith-initial query corresponds to ξ = ei, that is, to the ith-coordinate projection, and the reference vector x is uniformly random. (Section 4); The total number of queries was 24, of which 6 were initial queries. The ith-initial query corresponded to ξ = ei, that is, to the ith-coordinate projection. The initial values for the reference coordinate vector x were fxed to the same value across all user sessions. For acquisition, we used the expected improvement by projective preferential query. (Section 5) |