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..
Out-Of-Domain Unlabeled Data Improves Generalization
Authors: seyed amir hossein saberi, Amir Najafi, Alireza Heidari, Mohammad Hosein Movasaghinia, Abolfazl Motahari, Babak Khalaj
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | From a theoretical standpoint, we apply our framework on the classification problem of a mixture of two Gaussians in Rd... We validate our claims through experiments conducted on a variety of synthetic and real-world datasets. |
| Researcher Affiliation | Academia | Department of Electrical Engineering, Department of Computer Engineering, Sharif Center for Information Systems and Data Science, Sharif Institute for Convergence Science & Technology, Sharif University of Technology, Tehran, Iran |
| Pseudocode | Yes | Algorithm 1 Finding the adversarial perturbed input for original input data based on gradient ascent |
| Open Source Code | No | The paper does not provide a direct link to a source-code repository nor explicitly states that the code for their method is being released. |
| Open Datasets | Yes | NCT-CRC-HE-100K consists of 100,000 histopathology images of colon tissue (Katherm et al., 2018). |
| Dataset Splits | No | Finally, we select a combination of hyper-parameters that achieved the highest accuracy on a validation dataset, and we report the accuracy of our model, using these hyper-parameters, on the test samples. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., GPU models, CPU models, or memory) used to run its experiments. |
| Software Dependencies | Yes | The codes are written using the Python programming language and the Pytorch 2.0 machine learning framework. |
| Experiment Setup | Yes | A random search process has been performed to find the optimum γ, γ , λ, and weight-decay. Finally, we select a combination of hyper-parameters that achieved the highest accuracy on a validation dataset, and we report the accuracy of our model, using these hyper-parameters, on the test samples. |