Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Relaxed Gaussian Process Interpolation: a Goal-Oriented Approach to Bayesian Optimization
Authors: Sébastien J. Petit, Julien Bect, Emmanuel Vazquez
JMLR 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments indicate that using re GP instead of stationary GP models in Bayesian optimization is beneficial. In this section, we run numerical experiments to demonstrate the interest of using EGO-R instead of EGO for minimization problems. The optimization algorithms are tested against a benchmark of test functions from Surjanovic and Bingham (2013) summarized in Table 1, with nrep = 100 (random) repetitions, and a budget of ntot = 300 evaluations for each repetition. |
| Researcher Affiliation | Academia | S ebastien J. Petit EMAIL Laboratoire National de M etrologie et d Essais, 78197, Trappes Cedex, France Julien Bect EMAIL Universit e Paris-Saclay, CNRS, Centrale Sup elec, Laboratoire des signaux et syst emes, 91190, Gif-sur-Yvette, France Emmanuel Vazquez EMAIL Universit e Paris-Saclay, CNRS, Centrale Sup elec, Laboratoire des signaux et syst emes, 91190, Gif-sur-Yvette, France |
| Pseudocode | Yes | Algorithm 1 re GP with automatic selection of the relaxation range. Input: Data (xn, zn); a range of interest Q; and a list R\Q = R(0) \dots R(G \_ 1) = /0 of relaxation range candidates. for g = 0 to G \_ 1 do Obtain b\theta (g) n and z(g) n by solving (14) with R(g) Compute Jn(R(g)) with Q, b\theta (g) n , and z(g) n using (26) end for Output: The pair b\theta (g) n , z(g) n that minimizes (26). |
| Open Source Code | Yes | An open source implementation of the re GP method and the numerical experiments is available online at https://github.com/relaxed GP/regp_paper_experiments. |
| Open Datasets | Yes | The optimization algorithms are tested against a benchmark of test functions from Surjanovic and Bingham (2013) summarized in Table 1, with nrep = 100 (random) repetitions, and a budget of ntot = 300 evaluations for each repetition. |
| Dataset Splits | No | The paper describes Bayesian optimization, which involves sequentially selecting evaluation points rather than using pre-defined dataset splits. It mentions an 'initial design of size n0 = 10d' and 'a budget of ntot = 300 evaluations', which refer to the number of sequentially acquired evaluation points, not a conventional train/test/validation split of a static dataset. |
| Hardware Specification | No | No specific hardware details such as GPU/CPU models, processors, or memory specifications are provided for the experiments. |
| Software Dependencies | No | The paper mentions 'The Sci Py implementation was used with default parameters' and refers to a 'sequential Monte Carlo approach' but does not specify version numbers for SciPy or any other software dependencies. |
| Experiment Setup | Yes | For all four algorithms, we use an initial design of size n0 = 10d, and we consider GPs with a constant mean function and a Mat ern covariance function with regularity \nu = 5/2. The maximization of the sampling criteria (5) and (27) is performed using a sequential Monte Carlo approach (Benassi et al., 2012; Feliot et al., 2017). The optimization algorithms are tested against a benchmark of test functions from Surjanovic and Bingham (2013) summarized in Table 1, with nrep = 100 (random) repetitions, and a budget of ntot = 300 evaluations for each repetition. ... In this article, the numerical experiments were conducted with \alpha = 0.25. ... (with G = 10 in the experiments below). When applied to UCB, all re GP variants outperform the standard UCB algorithm on Beale, Sixhump Camel, Dixon-Price (4) and (10), Goldstein-Price, all instances of Perm and Rosenbrock, Three-hump Camel, Zakharov (4) and (6). |