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..
On Finite-Sample Identifiability of Contrastive Learning-Based Nonlinear Independent Component Analysis
Authors: Qi Lyu, Xiao Fu
ICML 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical experiments are used to validate the theorems. In this section, we validate our theoretical results using synthetic and real data experiments. Fig. 2 shows the n ICA performance in terms of MI using different network width R s under N= 5,000 and N= 10,000. Fig. 4 shows the averaged classification errors using SVM and logistic regression, respectively. |
| Researcher Affiliation | Academia | Qi Lyu 1 Xiao Fu 1 1School of EECS, Oregon State University, Corvallis, OR, United States. Correspondence to: Xiao Fu <EMAIL>, Qi Lyu <EMAIL>. |
| Pseudocode | No | The paper does not contain any sections, figures, or blocks explicitly labeled as "Pseudocode" or "Algorithm". |
| Open Source Code | No | The paper does not contain any statements or links indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | In addition to synthetic data, we also use the EEG eye dataset from the UCI repository (Dua & Graff, 2017). |
| Dataset Splits | No | We use 12,000 data samples as the training set to learn h( ). Then, we train simple classifiers (i.e., SVM and logistic regression) using bs = h(x). The classifiers are tested using 3000 test samples. The paper specifies training and test sets, but no explicit validation set. |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., GPU/CPU models, memory, or cloud computing instances) used for running the experiments. |
| Software Dependencies | No | For optimization, we use the Adam optimizer (Kingma & Ba, 2014) with an initial learning rate 5 10 4. We model h( ) and phi( ) using three-hidden-layer neural networks. The activation function is Re LU. we estimate the MI using kernel density estimation (Kozachenko & Leonenko, 1987). We compute the MI between each of the recovered by and the ground truth s s. Then, we use the Hungarian algorithm (Kuhn, 1955) to fix the permutation ambiguity. No specific version numbers for software are provided. |
| Experiment Setup | Yes | We model h( ) and phi( ) using three-hidden-layer neural networks. We test various R s (i.e., the number of hidden neurons) for each layer, where R {4, 8, 16, 32, 64, 128, 256, 512}. The activation function is Re LU. For optimization, we use the Adam optimizer (Kingma & Ba, 2014) with an initial learning rate 5 10 4. |