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..
A Birth-Death Process for Feature Allocation
Authors: Konstantina Palla, David Knowles, Zoubin Ghahramani
ICML 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 6. Experiments We experimentally evaluate the BEP model on real-world genomics and social network data. To evaluate the model ๏ฌt, we compared the BEP model to independent models at each time point. |
| Researcher Affiliation | Collaboration | 1University of Oxford, Oxford, UK 2Stanford University, California, USA 3University of Cambridge, Cambridge, UK 4Uber AI Labs, SF, California, USA. |
| Pseudocode | No | The paper describes the model and processes mathematically but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statements about making the source code available or provide links to a code repository. |
| Open Datasets | Yes | Here we used a subset of the gene expression data from Piechota et al. (2010)..., For this experiment we used Ch IP-seq (chromatin immunoprecipitation sequencing) data downloaded from the ENCODE project (Consortium, 2007), In van de Bunt et al. (1999), 32 university freshman students... |
| Dataset Splits | Yes | We created 7 train-test splits holding out 20% of the data... and We ran 7 different held-out tests, holding out a different 20% of the data each time. and holding out 10% of all links across all time points. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running the experiments. |
| Software Dependencies | No | The paper mentions employing Markov Chain Monte Carlo (MCMC) for posterior inference but does not provide specific software names with version numbers for implementation or dependencies. |
| Experiment Setup | Yes | We created 7 train-test splits holding out 20% of the data, and ran 700 MCMC iterations. and a burnin of 500. and We choose a Gaussian prior over A, i.e Afm N(0, 1). and we assume the priors wt(k, l) N ยตw, ฯ2 w and s N ยตs, ฯ2 s . |