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..
Lost Relatives of the Gumbel Trick
Authors: Matej Balog, Nilesh Tripuraneni, Zoubin Ghahramani, Adrian Weller
ICML 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conducted experiments with the following aims: |
| Researcher Affiliation | Collaboration | 1University of Cambridge, UK 2MPI-IS, T ubingen, Germany 3UC Berkeley, USA 4Uber AI Labs, USA 5Alan Turing Institute, UK. |
| Pseudocode | Yes | Algorithm 1 Sequential sampler for Gibbs distribution |
| Open Source Code | Yes | Code: https://github.com/matejbalog/gumbel-relatives. |
| Open Datasets | Yes | Figure 4 shows the MSEs of U( ) as estimators of ln Z on 10 10 (n = 100) binary pairwise grid models with unary potentials sampled uniformly from [ 1, 1] and pairwise potentials from [0, C] (attractive models) or from [ C, C] (mixed models), for varying coupling strengths C. |
| Dataset Splits | No | The paper does not provide specific training/validation/test dataset splits with percentages or sample counts. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions "using lib DAI (Mooij, 2010)" but does not specify a version number for this or any other software dependency. |
| Experiment Setup | Yes | Figure 4 shows the MSEs of U( ) as estimators of ln Z on 10 10 (n = 100) binary pairwise grid models with unary potentials sampled uniformly from [ 1, 1] and pairwise potentials from [0, C] (attractive models) or from [ C, C] (mixed models), for varying coupling strengths C. We replaced the expectations in U( ) s with sample averages of size M = 100, using lib DAI (Mooij, 2010) to solve the MAP problems yielding these samples. |