A Sparsity Principle for Partially Observable Causal Representation Learning
Authors: Danru Xu, Dingling Yao, Sebastien Lachapelle, Perouz Taslakian, Julius Von Kügelgen, Francesco Locatello, Sara Magliacane
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we propose two methods that implement these theoretical results and validate their effectiveness with experiments on simulated data and image benchmarks, e.g. Causal3DIdent (von Kügelgen et al., 2021), that we modify to test our partial observability setting. |
| Researcher Affiliation | Collaboration | 1University of Amsterdam 2Institute of Science and Technology Austria 3Max Planck Institute for Intelligent Systems, Tübingen, Germany 4Samsung SAIT AI Lab, Montreal 5Mila, Université de Montréal 6Service Now Research 7Seminar for Statistics, ETH Zürich. |
| Pseudocode | No | The paper does not include any explicitly labeled 'Pseudocode' or 'Algorithm' blocks, nor does it present structured algorithmic steps outside of narrative text. |
| Open Source Code | Yes | We provide all the code for our method and the experiments at https://github.com/danrux/sparsity-crl. |
| Open Datasets | Yes | Experiments on different simulated datasets and established benchmarks highlight the effectiveness of our approach in recovering the ground-truth latents. (...) e.g. Causal3DIdent (von Kügelgen et al., 2021) (...) We provide all the code for our method and the experiments at https://github.com/danrux/sparsity-crl. |
| Dataset Splits | No | The paper mentions 'generate images online until convergence' for the multiple balls dataset and samples from Causal3DIdent, but it does not specify explicit train/validation/test dataset splits with percentages, sample counts, or citations to predefined splits for reproducibility. |
| Hardware Specification | No | The paper mentions support in using 'the Dutch National Supercomputer Snellius' in the acknowledgments, but it does not provide specific hardware details such as GPU/CPU models, processor types, or memory amounts used for the experiments. |
| Software Dependencies | No | The paper mentions software and libraries like 'Cooper' and 'Py Game (Shinners, 2011)' but does not provide specific version numbers for these or other key software components, which is necessary for full reproducibility. |
| Experiment Setup | Yes | For the encoder g and the decoder ˆf, we use 7-layer MLPs with [10, 50, 50, 50, 50, 10] n units per layer, where n is the number of causal variables, with Leaky Re LU activations for each layer except the final one in the piecewise linear case. (...) In our experiments, we use ϵ = 0.01 or 0.001, details are provided in App. D.2. (...) We solve both problems with the extra-gradient version of Adam (Gidel et al., 2018). (...) Primal optimizer learning rate 1e-4, Dual optimizer learning rate 1e-4/2, Batch size 6144 (Table 4). |