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..
The Star Geometry of Critic-Based Regularizer Learning
Authors: Oscar Leong, Eliza O'Reilly, Yong Sheng Soh
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also experimentally show that such losses can be competitive for learning regularizers in a simple denoising setting. An empirical comparison between neural network-based regularizers learned using these losses and the adversarial loss is presented in Section 3.1. |
| Researcher Affiliation | Academia | Oscar Leong Department of Statistics and Data Science University of California, Los Angeles EMAIL Eliza O Reilly Department of Applied Mathematics and Statistics Johns Hopkins University EMAIL Yong Sheng Soh Department of Mathematics National University of Singapore EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | No statement or link indicating open-source code availability for the described methodology. |
| Open Datasets | Yes | To do this, we consider denoising on the MNIST dataset [50]. We take 10000 random samples from the MNIST training set (constituting our Dr distribution) and add Gaussian noise with variance σ2 = 0.05 (constituting our Dn distribution). |
| Dataset Splits | No | The paper does not explicitly mention a validation set or provide details on how data was split for validation. |
| Hardware Specification | Yes | The experiments were run on a single NVIDIA A100 GPU. |
| Software Dependencies | No | The paper mentions the use of the Adam optimizer, but does not provide specific version numbers for any software dependencies or libraries. |
| Experiment Setup | Yes | They were trained using the adversarial loss and Hellinger-based loss (5). We also used the gradient penalty term from [53] for both losses. We used the Adam optimizer for 20000 epochs and learning rate 10 3. We ran gradient descent for 2000 iterations with a learning rate of 10 3. For the choice of regularization parameter λ, we note that in [53], the authors fix this value to be λ := 2 λ where λ := EN(0,σ2I)[ z ℓ2] as the regularizer that achieves a small gradient penalty will be (approximately) 1-Lipschitz. For the Hellinger-based network, we found that λ = 5.1 λ2 gave better performance, so we used this for recovery. We additionally tune the regularization strength for the adversarially trained regularizer and found λ = 0.75 λ performed better than the original fixed value. |