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..
Information-Theoretic Analysis of Unsupervised Domain Adaptation
Authors: Ziqiao Wang, Yongyi Mao
ICLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | This paper uses information-theoretic tools to analyze the generalization error in unsupervised domain adaptation (UDA). We present novel upper bounds for two notions of generalization errors. [...] Specifically, we present two simple techniques for improving generalization in UDA and validate them experimentally. [...] Experiments are performed to verify the effectiveness of these strategies. |
| Researcher Affiliation | Academia | Ziqiao Wang & Yongyi Mao University of Ottawa EMAIL |
| Pseudocode | Yes | Algorithm 1 Controlling Label Information (Appendix D.2, page 28) |
| Open Source Code | No | Appendix D states: 'Our code builds largely on the implementation from Gulrajani & Lopez-Paz (2021)3 and Nguyen et al. (2022)4.' Footnotes 3 and 4 provide GitHub links to these existing external implementations. The paper does not explicitly state that *their* specific modifications or code for this paper are released or available. |
| Open Datasets | Yes | Datasets We select two popular small datasets, Rotated MNIST and Digits, to compare the different methods. Rotated MNIST is built based on the MNIST dataset (Le Cun et al., 2010) [...] Digits consists of three sub-datasets, namely MNIST, USPS (Hull, 1994) and SVHN (Netzer et al., 2011) |
| Dataset Splits | Yes | Every dataset has a validation set, and the model selection scheme is based on the best performance achieved on the validation set of target domain during training (oracle). |
| Hardware Specification | Yes | We implemented our approach using Py Torch (Paszke et al., 2019) and conducted all experiments on NVIDIA Tesla V100 GPUs with 32 GB of memory. |
| Software Dependencies | No | The paper mentions 'Py Torch (Paszke et al., 2019)' which implies a version from the citation year, but it does not explicitly state a specific version number (e.g., PyTorch 1.9). Python is also mentioned without a version. |
| Experiment Setup | Yes | Other settings are also the same as Gulrajani & Lopez-Paz (2021) and Nguyen et al. (2022), for example, each algorithm is trained for 100 epochs. To select the hyperparameters (λ1 and λ2) for ERM-GP, ERM-KL, KL-GP and KL-CL, we perform random search. Specifically, λ1 is searched between [0.1, 0.9] and λ2 is searched between [10 6, 0.8]. |