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..
Variance-Reducing Couplings for Random Features
Authors: Isaac Reid, Stratis Markou, Krzysztof Choromanski, Richard E Turner, Adrian Weller
ICLR 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We test our algorithms on UCI datasets and real-world graphs, verifying that OT couplings substantially reduce kernel estimator variance (Secs 3 and 4). |
| Researcher Affiliation | Collaboration | 1University of Cambridge, 2Google Deep Mind, 3Columbia, 4Alan Turing Institute |
| Pseudocode | No | The paper describes methods and mathematical formulations in prose and equations, but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available at: https://github.com/cambridge-mlg/learnable-qmc. |
| Open Datasets | Yes | We test our algorithms on UCI datasets and real-world graphs... Table 1: Performance of RFFs and RLFs on kernel estimation with UCI datasets... Fig. 1 demonstrates this on a train-test split of the POWER dataset... Performers trained on Image Net (Deng et al., 2009)... Fig. 3 shows the results for cora (N = 2708)... We use mesh graphs made available by Dawson-Haggerty (2023)... traffic flow dataset of the highways of San Jose, California, curated by Borovitskiy et al. (2021) using data from Chen et al. (2001) and Open Street Map. |
| Dataset Splits | Yes | For each dataset, we conduct cross validation with 20 splits, splitting each dataset into a training and a test set... We set them to a maximum of 256 points each by sub-sampling at random without replacement. After training the GP, we evaluate the metrics on the test set, and repeat this procedure for all 20 splits. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, processor types, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions using 'Adam optimiser (Kingma and Ba, 2014)' but does not provide specific software dependencies with version numbers (e.g., Python 3.8, PyTorch 1.9, or specific library versions). |
| Experiment Setup | Yes | We train the exact GP using the Adam optimiser (Kingma and Ba, 2014), using a learning rate of 10^-2. The exact GP optimisation stage converges around 1000 steps, and we run it up to 5000 steps. We use a transformer with 12 layers and 12 heads, with hidden size 768 and MLP dimension 3072. We take 16x16 patches, and train with the Adam optimiser for 90 epochs with a compound learning rate (10^4 steps linear warmup, constant, then cosine decay, with base LR 3e-3 and final LR 1e-5). The batch size is 4096. |