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..
Kernelized Normalizing Constant Estimation: Bridging Bayesian Quadrature and Bayesian Optimization
Authors: Xu Cai, Jonathan Scarlett
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our findings are supported by both algorithm-independent lower bounds and algorithmic upper bounds, as well as simulation studies conducted on a variety of benchmark functions. |
| Researcher Affiliation | Academia | 1 Department of Computer Science, National University of Singapore 2 Department of Mathematics, Institute of Data Science, National University of Singapore |
| Pseudocode | Yes | Algorithm 1: Two-batch normalizing constant estimation algorithm |
| Open Source Code | No | The paper does not provide any explicit statements or links to open-source code for the described methodology. |
| Open Datasets | Yes | Benchmark functions. Exact formulations of functions including, Ackley, Alpine, Product-Peak, Zhou, etc., can be found in (Bingham 2013). Virtual Library of Simulation Experiments: Test Functions and Datasets. https://www.sfu.ca/~ssurjano/index.html. Accessed: 2023-08-05. |
| Dataset Splits | No | The paper mentions allocating samples for different batches (e.g., 'T/2 samples') and discusses time horizons, but it does not specify explicit training, validation, or test dataset splits in terms of percentages or sample counts for the data used in experiments. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments. |
| Software Dependencies | No | The paper mentions 'built-in Sci Py optimizer based on L-BFGS-B' and 'Langevin Monte Carlo (LMC)' but does not specify version numbers for these or any other software components or libraries. |
| Experiment Setup | Yes | For all functions considered in this section, we consider a time horizon of T = 256, λ 2 {0.5, 5, 10}, σ 2 {0, 0.01, 0.1} and 2 {0.5, 1.5, 2.5}. The total number of steps of (6) is set as 20, and the LMC learning rate is β = 10 3. |