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..
DR-ABC: Approximate Bayesian Computation with Kernel-Based Distribution Regression
Authors: Jovana Mitrovic, Dino Sejdinovic, Yee-Whye Teh
ICML 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4. Experimental Results Toy example. The first problem we study is the following Gaussian hierarchical model... In our experiments, we compare full and conditional DR-ABC against SA-ABC and K2-ABC. ... For the performance metric, we calculate the mean square error (MSE) of the parameter of interest on synthetic data. ... Figure 1 describes the performance of our chosen methods across different numbers of particles... |
| Researcher Affiliation | Academia | Jovana Mitrovic EMAIL Dino Sejdinovic EMAIL Yee Whye Teh EMAIL Department of Statistics, University of Oxford |
| Pseudocode | Yes | Algorithm 1 Distribution Regression... Algorithm 2 Conditional Distribution Regression... Algorithm 3 DR-ABC Algorithm |
| Open Source Code | Yes | The code for all presented experiments is available at https://github.com/jovana-mitrovic/dr-abc. |
| Open Datasets | Yes | As an example of an ecological system with a dynamic generative process, we examine the problem of inferring the dynamics of the adult blowfly population as introduced in Wood (2010). |
| Dataset Splits | Yes | The hyperparameters in the two DRABC methods are set via five-fold cross-validation on appropriately defined grids. |
| Hardware Specification | No | The paper discusses computational complexity and efficiency (e.g., 'significantly reduced computational cost'), but it does not specify any hardware details such as GPU or CPU models used for the experiments. |
| Software Dependencies | No | The paper refers to code availability on GitHub but does not explicitly list any software dependencies with specific version numbers (e.g., Python version, specific libraries, or frameworks). |
| Experiment Setup | Yes | The hyperparameters in the two DRABC methods are set via five-fold cross-validation on appropriately defined grids. For the grids of the different kernel bandwidth parameters, we multiply the respective median heuristics (Reddi et al., 2014) with a set of ten equally spaced points between 10 4 and 1000. For λ and ϵ, we choose the grids by exponentiating 10 to the powers given by ten equally spaced points between 4 and 1. ... We use either 100 or 200 particles in (conditional) distribution regression. |