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].
CMA-ES with Optimal Covariance Update and Storage Complexity
Authors: Oswin Krause, Dídac Rodríguez Arbonès, Christian Igel
NeurIPS 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We compared the Cholesky-CMA-ES with other CMA-ES variants.We provide empirical performance results comparing the original CMA-ES with the new Cholesky-CMA-ES using various benchmark functions in section 4. |
| Researcher Affiliation | Academia | Oswin Krause Dept. of Computer Science University of Copenhagen Copenhagen, Denmark EMAIL.dkDídac R. Arbonès Dept. of Computer Science University of Copenhagen Copenhagen, Denmark EMAIL Igel Dept. of Computer Science University of Copenhagen Copenhagen, Denmark EMAIL |
| Pseudocode | Yes | Algorithm 1: The Cholesky-CMA-ES.Algorithm 2: rank One Update(A, β, v) |
| Open Source Code | Yes | We added our algorithm to the open-source machine learning library Shark [Igel et al., 2008] and used LAPACK for high efficiency. |
| Open Datasets | No | We considered standard benchmark functions for derivative-free optimization given in Table 1.The paper uses mathematical benchmark functions (Sphere, Rosenbrock, etc.) which are defined by equations, not public datasets that would require specific links or citations for access. |
| Dataset Splits | No | The paper describes running 100 trials from different initial points and monitoring metrics. However, it does not specify explicit training, validation, or test dataset splits as it evaluates optimization algorithms on mathematical benchmark functions, not fixed datasets. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions using 'Shark [Igel et al., 2008]' and 'LAPACK', but does not provide specific version numbers for these software components. |
| Experiment Setup | Yes | All parameters (µ, λ, ω, cσ, dσ, cc, c1, cµ) are set to their default values [Hansen, 2015, Table 1].All starting points were drawn uniformly from [0, 1], except for Sphere, where we sampled from N(0, I).For each function, we vary d {4, 8, 16, . . . , 256}. |