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..
Adversarial Support Alignment
Authors: Shangyuan Tong, Timur Garipov, Yang Zhang, Shiyu Chang, Tommi S. Jaakkola
ICLR 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We quantitatively evaluate the method across domain adaptation tasks with shifts in label distributions. Our experiments show that the proposed method is more robust against these shifts than other alignment-based baselines. |
| Researcher Affiliation | Collaboration | Shangyuan Tong MIT CSAIL Timur Garipov MIT CSAIL Yang Zhang MIT-IBM Watson AI Lab Shiyu Chang UC Santa Barbara Tommi Jaakkola MIT CSAIL |
| Pseudocode | Yes | Algorithm 1 Our proposed ASA algorithm. |
| Open Source Code | Yes | We provide the code reproducing experiment results at https://github.com/timgaripov/asa. |
| Open Datasets | Yes | We use USPS (Hull, 1994) and MNIST (Le Cun et al., 1998) datasets for this adaptation problem. We use STL (Coates et al., 2011) and CIFAR-10 (Krizhevsky, 2009) for this adaptation task. We use train and validation sets of the Vis DA-17 challenge (Peng et al., 2017). |
| Dataset Splits | Yes | We use train and validation sets of the Vis DA-17 challenge (Peng et al., 2017). |
| Hardware Specification | Yes | The computational experiments presented in this paper were performed on Satori cluster developed as a collaboration between MIT and IBM. |
| Software Dependencies | No | The paper mentions 'Weights & Biases (Biewald, 2020)' but does not specify a version number for this or any other software dependencies. |
| Experiment Setup | Yes | We train all methods for 65 000 steps with batch size 64. We train the feature extractor, the classifier, and the discriminator with SGD (learning rate 0.02, momentum 0.9, weight decay 5 10 4). |