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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Quasi-Monte Carlo Features for Kernel Approximation
Authors: Zhen Huang, Jiajin Sun, Yian Huang
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In practice, the QMC kernel approximation approach is easily implementable and shows superior performance, as supported by the empirical evidence provided in the paper. |
| Researcher Affiliation | Academia | Zhen Huang 1 Jiajin Sun 1 Yian Huang 1 1Department of Statistics, Columbia University, New York, NY 10027, USA. |
| Pseudocode | No | The paper describes methods and theoretical derivations but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any statements about releasing source code or provide links to a code repository for the described methodology. |
| Open Datasets | Yes | We consider two choices of kernels: (i) the min kernel K(x, x ) = Qd i=1 min(xi, x i), and (ii) the Gaussian kernel K(x, x ) = exp( 1 2σ2 x x 2 2)... Cadata (Pace & Barry, 1997): In this data set (n = 20640, d = 6)... Cod-rna (Uzilov et al., 2006): This benchmark dataset (n = 59535 (train) + 271617 (test), d = 8)... |
| Dataset Splits | No | The paper mentions 'training samples' and 'test data points' with specific sizes for synthetic data, and 'random train-test split, allocating 25% of the data to the test set' for Cadata, and explicit train/test sizes for Cod-rna, but does not explicitly describe a separate validation split. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for experiments, such as CPU/GPU models, memory, or specific cloud instance types. It only mentions general terms like 'training samples' and 'data points'. |
| Software Dependencies | Yes | Halton sequence implemented in the Sci Py package in Python (Virtanen et al., 2020) is used. |
| Experiment Setup | Yes | The training and test data are generated from Y = f(X) + ε, where f is the regression function, X Unif[0, 1]d, and ε N(0, 1). We consider two choices of kernels: (i) the min kernel K(x, x ) = Qd i=1 min(xi, x i), and (ii) the Gaussian kernel K(x, x ) = exp( 1 2σ2 x x 2 2), with the bandwidth σ set as the median of X X (computed numerically)... The kernel ridge regularization parameter is set as λ = 0.25n 1 2r+1 . |