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..
Active Slices for Sliced Stein Discrepancy
Authors: Wenbo Gong, Kaibo Zhang, Yingzhen Li, Jose Miguel Hernandez-Lobato
ICML 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on goodness-of-fit tests and model learning show that our approach achieves both improved performance and faster convergence. |
| Researcher Affiliation | Academia | 1Department of Engineering, University of Cambridge, Cambridge, United Kingdom 2Department of Computing, Imperial College London, London, United Kingdom. |
| Pseudocode | Yes | Algorithm 1 Active slice algorithm |
| Open Source Code | No | The paper does not explicitly state that the source code for their methodology is made open or provide a link to it. |
| Open Datasets | Yes | The 3 GOF test benchmarks, with details in appendix H.1, are: (1) Laplace: p(x) = N(0, I), q(x) = QD d=1 Lap(xd|0, 1/ 2); (2) Multivariate-t: p(x) = N(0, 5 3I), q(x) is a fully factorized multivariate-t with 5 degrees of freedom, 0 mean and scale 1; (3) Diffusion: p(x) = N(0, I), q(x) = N(0, Σ1) where in q(x) the variance of 1st-dim is 0.3 and the rest is I. |
| Dataset Splits | Yes | For all methods requiring GO or active slices, we split the 1000 test samples from q into 800 test and 200 training data, where we run GO or active slice method on the training set. |
| Hardware Specification | No | The paper does not provide specific hardware specifications (e.g., GPU models, CPU types) used for running the experiments. |
| Software Dependencies | No | The paper does not specify software dependencies with version numbers. |
| Experiment Setup | Yes | For all methods requiring GO or active slices, we split the 1000 test samples from q into 800 test and 200 training data, where we run GO or active slice method on the training set. SKSD-g+GO with 1000 training epochs still exhibits a decreasing test power in Laplace and multivariate-t. On the other hand, SKSD-g+KE+GO with 50 training epochs has nearly optimal performance. [...] SKSD-rg+GO runs 50 training epochs with r and gr initialized to I. |