Kernelized Normalizing Constant Estimation: Bridging Bayesian Quadrature and Bayesian Optimization
Authors: Xu Cai, Jonathan Scarlett
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | 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. |