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 Spectral Approach to Gradient Estimation for Implicit Distributions
Authors: Jiaxin Shi, Shengyang Sun, Jun Zhu
ICML 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the proposed approach on both toy problems and real-world examples. The latter includes applications of SSGE to two widely used inference methods: Hamiltonian Monte Carlo and variational inference. ... In Figure 1 we plot the gradients estimates produced by the Stein gradient estimator, its out-of-sample extension (Stein+) (see Section 2.2), and our approach (SSGE). ... The average acceptance ratios over 10 runs are plotted in Figure 2b. We can see that SSGE clearly outperforms Stein+ and is even better than the KMC algorithm... |
| Researcher Affiliation | Academia | 1Dept. of Comp. Sci. & Tech., BNRist Center, State Key Lab for Intell. Tech. & Sys., THBI Lab, Tsinghua University 2Dept. of Comp. Sci., University of Toronto. Correspondence to: Jiaxin Shi <EMAIL>, Jun Zhu <EMAIL>. |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available at https://github.com/thjashin/ spectral-stein-grad. |
| Open Datasets | Yes | We follow the settings in Sejdinovic et al. (2014); Strathmann et al. (2015) and consider a Gaussian Process classification problem on the UCI Glass dataset. ... we adopt the settings in Shi et al. (2018) and train a deep convolutional VAE with implicit variational posteriors (Implicit VAE for short) on the Celeb A dataset. ... Besides the Celeb A experiments, we also tested the models on MNIST dataset and evaluated the test log likelihoods. |
| Dataset Splits | No | The paper does not explicitly state specific dataset split information (e.g., percentages or sample counts) for training, validation, or test sets. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory amounts, or detailed computer specifications) used for running its experiments are provided in the paper. |
| Software Dependencies | No | The paper mentions 'Implementations are based on Zhu Suan (Shi et al., 2017)' but does not provide specific version numbers for software dependencies like programming languages, libraries, or frameworks. |
| Experiment Setup | Yes | For the regularization coefficient η in eq. (10), we searched it in {0.001, 0.01, 0.1, 1, 10, 100} and plot the best result at η = 0.1. For SSGE, we set J = 6. ... For SSGE, we set J = 100. ... All other methods are trained with 100 samples for 20k iterations using Adam optimizer (Kingma & Ba, 2014). For SSGE, we set M = 100, and r = 0.99. ... we randomly uses between 1 and 10 leapfrog steps of size chosen uniformly in [0.01, 0.1], and a standard Gaussian momentum. |