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].
Stochastic Discrete Clenshaw-Curtis Quadrature
Authors: Nico Piatkowski, Katharina Morik
ICML 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments confirm that the new randomized algorithm is highly accurate if the parameter norm is small, and is otherwise comparable to methods with unbounded error. 5. Numerical Evaluation For our experiments, we implemented DCCQ (Alg. 1) and SDCCQ (Alg. 2) and execute them on a machine with 40 E5-2697 Xeon CPU cores. |
| Researcher Affiliation | Academia | Nico Piatkowski EMAIL Artificial Intelligence Group, TU Dortmund, Germany Katharina Morik EMAIL Artificial Intelligence Group, TU Dortmund, Germany |
| Pseudocode | Yes | Algorithm 1 DCCQ Input: θ Rd, k N, φ Output: Approximate partition function ˆZk(θ) and Algorithm 2 SDCCQ Input: θ Rd, k N, m Nk, φ Output: Approximate partition function Zm k (θ) |
| Open Source Code | Yes | Our C++ source code and the precomputed Qφ(i) values are available at http://sfb876.tu-dortmund.de/sdccq. |
| Open Datasets | No | The paper describes how parameters and data were generated for experiments (e.g., 'random Gaussian parameters', 'Ising grid models with randomly generated parameter vectors'), but does not provide concrete access information (link, DOI, formal citation) for a publicly available or open dataset. |
| Dataset Splits | No | The paper mentions 'averaged over 5-folds of SDCCQ' but does not provide specific details on how data was split into training, validation, or test sets (e.g., percentages, sample counts, or references to predefined splits). |
| Hardware Specification | Yes | For our experiments, we implemented DCCQ (Alg. 1) and SDCCQ (Alg. 2) and execute them on a machine with 40 E5-2697 Xeon CPU cores. |
| Software Dependencies | No | The paper mentions 'Our C++ source code' but does not provide specific ancillary software or library names with version numbers (e.g., 'PyTorch 1.9', 'TensorFlow 2.x'). |
| Experiment Setup | Yes | SDCCQ has been run with mi = 103, i and k {1, 2, 4, 8}, where, due to space restrictions, the plot shows the average over all SDCCQ runs. Specifically, we have n = 10 10 binary variables x { 1, 1}n with weights θvu=xy = wvu whenever x = y and θvu=xy = wvu otherwise. In the attractive setting, the wvu are drawn from [0, ω]; in the mixed setting, from [ ω, ω]. Moreover, vertex weights θv=1 = θv= 1 = wv are sampled from [ κ, κ] with κ {0.1, 1.0}. |