Approximate Vanishing Ideal Computations at Scale
Authors: Elias Samuel Wirth, Hiroshi Kera, Sebastian Pokutta
ICLR 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We perform numerical experiments on data sets of up to two million samples, highlighting that OAVI is an excellent large-scale feature transformation method. 5 NUMERICAL EXPERIMENTS Unless noted otherwise, the setup for the numerical experiments applies to all experiments in the paper. |
| Researcher Affiliation | Academia | Elias Wirth Institute of Mathematics Berlin Institute of Technology Berlin, Germany wirth@math.tu-berlin.de Hiroshi Kera Graduate School of Engineering Chiba University Chiba, Japan kera.hiroshi@gmail.com Sebastian Pokutta Institute of Mathematics & AI in Society, Science, and Technology Berlin Institute of Technology & Zuse Institute Berlin Berlin, Germany pokutta@zib.de |
| Pseudocode | Yes | Algorithm 1: Oracle approximate vanishing ideal algorithm (OAVI) |
| Open Source Code | Yes | Our code is publicly available on Git Hub. |
| Open Datasets | Yes | Table 1: Overview of data sets. All data sets are binary classification data sets and are retrieved from the UCI Machine Learning Repository (Dua & Graff, 2017) and additional references are provided. |
| Dataset Splits | Yes | We tune the hyperparameters on the training data using threefold cross-validation. |
| Hardware Specification | Yes | Experiments are implemented in PYTHON and performed on an Nvidia Ge Force RTX 3080 GPU with 10GB RAM and an Intel Core i7 11700K 8x CPU at 3.60GHz with 64 GB RAM. |
| Software Dependencies | No | The paper mentions 'PYTHON' and the 'SCIKIT-LEARN software package' but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | For the CG variants, we set τ = 1, 000. The CG variants are run up to accuracy ϵ = 0.01 ψ and terminated early when less than 0.0001 ψ progress is made in the difference between function values, when the coefficient vector of a generator is constructed, or if we have a guarantee that no coefficient vector of a generator can be constructed. Table 3: Hyperparameter ranges for numerical experiments. |