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..
Kernel Recursive ABC: Point Estimation with Intractable Likelihood
Authors: Takafumi Kajihara, Motonobu Kanagawa, Keisuke Yamazaki, Kenji Fukumizu
ICML 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We have conducted a variety of numerical experiments, including parameter estimation for a real-world pedestrian flow simulator, and show that in most cases our method outperforms existing approaches. |
| Researcher Affiliation | Collaboration | 1NEC Corporation 2National Institute of Advanced Industrial Science and Technology 3Max Planck Institute for Intelligent Systems 4The Institute of Statistical Mathematics. |
| Pseudocode | Yes | Algorithm 1 Kernel Recursive ABC |
| Open Source Code | No | The paper mentions using 'publicly available code3' for comparison with a third-party method (Bayesian Optimization), but does not state that the code for their proposed Kernel Recursive ABC method is open-source or publicly available. |
| Open Datasets | Yes | Crowd Walk, a publicly available real-world simulator5 for the movements of pedestrians in a commercial district (Yamashita et al., 2010). Footnote 5: https://github.com/crest-cassia/CrowdWalk |
| Dataset Splits | Yes | That is, to evaluate one configuration of hyper-parameters, we first used 75% of the observed data for point estimation and then computed the discrepancy between the rest of the observed data and the ones simulated from point estimates |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running its experiments. |
| Software Dependencies | No | The paper mentions software like 'GPyOpt' (for Bayesian optimization) but does not provide specific version numbers for any software dependencies used in its experiments. |
| Experiment Setup | Yes | The bandwidth of a Gaussian kernel was selected from candidate values, each of which is the median (of pairwise distances) multiplied by logarithmically equally spaced values between 2 4 and 24 (Takeuchi et al., 2006, Sec. 5.1.1). Regularization constants for the proposed method and kernel ABC, as well as the soft threshold for K2-ABC, were selected from logarithmically spaced values between 10 4 and 1. |