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..
Detecting Statistical Interactions from Neural Network Weights
Authors: Michael Tsang, Dehua Cheng, Yan Liu
ICLR 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5 EXPERIMENTS In this section, we discuss our experiments on both simulated and real-world datasets to study the performance of our approach on interaction detection. |
| Researcher Affiliation | Academia | Michael Tsang, Dehua Cheng, Yan Liu Department of Computer Science University of Southern California EMAIL |
| Pseudocode | Yes | Algorithm 1 NID Greedy Ranking Algorithm |
| Open Source Code | No | The paper does not contain an explicit statement or link confirming the availability of the source code for the methodology described. |
| Open Datasets | Yes | We use four real-world datasets, of which two are regression datasets, and the other two are binary classification datasets. ... the cal housing dataset ... (Pace & Barry, 1997). The bike sharing dataset ... (Fanaee-T & Gama, 2014). The higgs boson dataset ... (Adam-Bourdarios et al., 2014). Lastly, the letter recognition dataset ... (Frey & Slate, 1991). |
| Dataset Splits | Yes | In all synthetic experiments, we used random train/valid/test splits of 1/3 each on 30k data points. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions software components like 'Re LU activation', 'backpropagation', 'MLP', 'MLP-M', and 'lasso', but does not provide specific version numbers for any libraries, frameworks, or solvers used. |
| Experiment Setup | Yes | In our experiments, all networks that model feature interactions consisted of four hidden layers with first-to-last layer sizes of: 140, 100, 60, and 20 units. In contrast, all individual univariate networks had three hidden layers with sizes of: 10, 10, and 10 units. All networks used Re LU activation and were trained using backpropagation. ... On the synthetic test suite, MLP and MLP-M were trained with L1 constants in the range of 5e-6 to 5e-4, based on parameter tuning on a validation set. On real-world datasets, L1 was fixed at 5e-5. MLP-Cutoff used a fixed L2 constant of 1e-4 in all experiments involving cutoff. Early stopping was used to prevent overfitting. |