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..
Sliced Mutual Information: A Scalable Measure of Statistical Dependence
Authors: Ziv Goldfeld, Kristjan Greenewald
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our theory is supported by numerical studies of independence testing and feature extraction, which demonstrate the potential gains SMI offers over classic MI for high-dimensional inference. ... We validate our theory on synthetic experiments, demonstrating that SMI is a scalable alternative to classic MI when dealing with high-dimensional data. ... 4 Empirical Results |
| Researcher Affiliation | Collaboration | Ziv Goldfeld Cornell University EMAIL Kristjan Greenewald MIT-IBM Watson AI Lab EMAIL |
| Pseudocode | Yes | Pseudocode and computational complexity for b SIn,m can be found in Section B of the supplement. |
| Open Source Code | No | The paper does not provide an explicit statement about releasing code for the methodology or a link to a code repository. |
| Open Datasets | Yes | Figure 4: Solution for optimizing A-transformed SMI of the 0-1 MNIST setup using S-MINE. Rows 0 and 1 of A are shown in (a) and (b), respectively. We next combine S-MINE with independence testing, looking to maximize the SMI using transformations AX, AY , where X, Y are samples from a random MNIST class (either 0 or 1) and A 2 R10 d. |
| Dataset Splits | No | The paper describes generating '50 datasets comprising n positive samples' and '50 more dataset of negative samples' for independence testing, and uses Monte-Carlo sampling. However, it does not specify explicit train/validation/test splits with percentages or counts for model training or evaluation in a reproducible manner. |
| Hardware Specification | No | The paper does not explicitly describe the hardware (e.g., specific GPU/CPU models, cloud resources with specifications) used to run its experiments. |
| Software Dependencies | No | The paper mentions using the 'Kozachenko Leonenko estimator', 'MINE', and 'S-MINE', but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | The MC step for SMI estimation (see (6)) uses 1000 random slices... with g in S-MINE realized as a two-layer fully connected neural network with 100 hidden units for. |