Robustness of Nonlinear Representation Learning
Authors: Simon Buchholz, Bernhard Schölkopf
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our analysis here focuses on theoretical questions regarding identifiability in misspecified settings, but this has nevertheless profound implications for the empirical side because it clarifies what assumptions generate or do not generate useful learning signals that can be exploited by suitable algorithms. This is particularly important since there is still a lack of algorithms that uncover the true latent structure for complex data beyond toy settings. In Appendix I we confirm empirically the theoretical convergence rates which we derived for perturbed linear ICA. |
| Researcher Affiliation | Academia | 1Max Planck Institute for Intelligent Systems, T ubingen, Germany 2T ubingen AI Center, T ubingen, Germany 3ELLIS Institute, T ubingen, Germany. Correspondence to: Simon Buchholz <sbuchholz@tue.mpg.de>. |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. It focuses on theoretical derivations and proofs. |
| Open Source Code | No | The paper does not include an explicit statement about releasing code or a link to a code repository for the described methodology. |
| Open Datasets | No | The paper mentions using a 'two-dimensional standard normal distribution' for latent variables (Gaussian latent variables Z) and 'Laplace distributions' for sources Si in the experimental illustration section. However, it does not provide concrete access information (like a link, DOI, or formal citation to a public dataset) for these or any other specific dataset used for experiments beyond stating their distribution type. |
| Dataset Splits | No | The paper does not provide specific details on training, validation, or test dataset splits. For the experimental illustrations, it mentions generating data from distributions but no split information. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU or CPU models, or memory specifications used for running the experiments. |
| Software Dependencies | No | The paper mentions using the Fast-ICA algorithm but does not specify any software dependencies with version numbers. |
| Experiment Setup | No | The paper states it uses the Fast-ICA algorithm with 'n = 10^6 samples' and initializes 'w_init = w_i' for the experimental illustration but does not provide specific hyperparameters like learning rates, batch sizes, or optimizer settings. It mentions 'sufficiently small step size' but no concrete values. |