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..
Gradient Estimators for Implicit Models
Authors: Yingzhen Li, Richard E. Turner
ICLR 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The efficacy of the proposed estimator is empirically demonstrated by examples that include gradient-free MCMC, meta-learning for approximate inference and entropy regularised GANs that provide improved sample diversity. |
| Researcher Affiliation | Academia | Yingzhen Li & Richard E. Turner University of Cambridge Cambridge, CB2 1PZ, UK EMAIL |
| Pseudocode | No | The paper describes methods through text and mathematical equations but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Implementation is released at https://github.com/Yingzhen Li/Stein Grad. |
| Open Datasets | Yes | We take from the UCI repository (Lichman, 2013) six binary classification datasets (australian, breast, crabs, ionosphere, pima, sonar) |
| Dataset Splits | Yes | We use ionosphere as the validation set to tune ζ. The remaining 4 datasets are further split into 40% training subset for simulating samples from the approximate sampler, and 60% test subsets for evaluating the sampler s performance. |
| Hardware Specification | Yes | All the methods are timed on a machine with an NVIDIA Ge Force GTX TITAN X GPU. |
| Software Dependencies | No | The paper mentions using 'Adam optimiser' but does not provide specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions or specific library versions). |
| Experiment Setup | Yes | For the training task... The step-size ζ is selected as 1e-5... The learning rate is set to 0.001, the number of epochs is set to 500... The minibatch size is set to K = 100. Learning rate is initialised at 0.0002 and decayed by 0.9 every 10 epochs... The selected γ and α values are: for KDE estimator approach γ = 0.3, αγ = 0.05, for score matching estimator approach γ = 0.3, αγ = 0.1, and for Stein approach γ = 0.5 and αγ = 0.3. |