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..
Identifiable Latent Polynomial Causal Models through the Lens of Change
Authors: Yuhang Liu, Zhen Zhang, Dong Gong, Mingming Gong, Biwei Huang, Anton van den Hengel, Kun Zhang, Javen Qinfeng Shi
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experimental results, obtained from both synthetic and real-world data, validate our theoretical contributions concerning identifiability and consistency. |
| Researcher Affiliation | Academia | 1 Australian Institute for Machine Learning, The University of Adelaide, Australia 2 School of Computer Science and Engineering, The University of New South Wales, Australia 3 School of Mathematics and Statistics, The University of Melbourne, Australia 4 Halicio glu Data Science Institute (HDSI), University of California San Diego, USA 5 Department of Philosophy, Carnegie Mellon University, USA 6 Mohamed bin Zayed University of Artificial Intelligence, United Arab Emirates |
| Pseudocode | No | Figure 8 depicts the proposed method to learn polynomial causal representations with non-Gaussian noise. Figure 9 depicts the proposed method to learn polynomial causal representations with Gaussian noise. |
| Open Source Code | No | The paper does not contain any explicit statement about releasing code or a link to a repository. |
| Open Datasets | Yes | Image Data We further verify the proposed identifiability results and method on images from the chemistry dataset proposed in Ke et al. (2021)... |
| Dataset Splits | No | Synthetic Data We first conduct experiments on synthetic data, generated by the following process: we divide latent noise variables into M segments, where each segment corresponds to one value of u as the segment label. Within each segment, the location and scale parameters are respectively sampled from uniform priors. |
| Hardware Specification | No | The paper does not provide any specific hardware details used for running the experiments. |
| Software Dependencies | No | Instead, we straightforwardly use the Py Torch (Paszke et al., 2017) implementation of the method of Jankowiak & Obermeyer (2018), which computes implicit reparameterization using a closed-form approximation of the probability density function derivative. |
| Experiment Setup | Yes | For experiments on the synthetic data and f MRI data, the encoder, decoder, MLP for λ, and MLP for prior are implemented by using 3-layer fully connected networks and Leaky-Re LU activation functions. For optimization, we use Adam optimizer with learning rate 0.001. |