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 [1].
Identifiability of deep generative models without auxiliary information
Authors: Bohdan Kivva, Goutham Rajendran, Pradeep Ravikumar, Bryon Aragam
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our main result is an identifiability hierarchy that significantly generalizes previous work and exposes how different assumptions lead to different strengths of identifiability, and includes certain vanilla VAEs with isotropic Gaussian priors as a special case. For example, our weakest result establishes (unsupervised) identifiability up to an affine transformation, and thus partially resolves an open problem regarding model identifiability raised in prior work. These theoretical results are augmented with experiments on both simulated and real data. |
| Researcher Affiliation | Academia | Bohdan Kivva University of Chicago EMAIL Goutham Rajendran University of Chicago EMAIL Pradeep Ravikumar Carnegie Mellon University EMAIL Bryon Aragam University of Chicago EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks in the main text. |
| Open Source Code | Yes | 3. If you ran experiments... (a) Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [Yes] |
| Open Datasets | Yes | Real data We measure stability of the learnt latent space by training MFCVAE [18] on MNIST 10 times with different initializations and then comparing the latent representations learnt. |
| Dataset Splits | Yes | 3. If you ran experiments... (b) Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [Yes] |
| Hardware Specification | No | 3. If you ran experiments... (d) Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)? [Yes] See supplement. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers in the main text. |
| Experiment Setup | No | Definitions of these metrics and additional details on the experiments can be found in Appendix J. |