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..
Overcoming Concept Shift in Domain-Aware Settings through Consolidated Internal Distributions
Authors: Mohammad Rostami, Aram Galstyan
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct experiments on five benchmarks and observe that our algorithm compares favorably against SOTA UDA methods.Empirical Evaluation: Since sequential model adaptation is not a well-explored problem, we follow the UDA literature for evaluation due to the topic proximity. |
| Researcher Affiliation | Academia | Information Sciences Institute, University of Southern California EMAIL |
| Pseudocode | Yes | Algorithm 1: SDAUP (λ, ITR) |
| Open Source Code | Yes | Our code is provided at https://github.com/rostami-m/SDAUP. |
| Open Datasets | Yes | We validate our method on five standard UDA benchmarks and adapted them for sequential task learning: Digit recognition tasks, Office-31 Dataset, Image CLEF-DA Dataset, Office-Caltech Dataset, and Vis DA-2017. Details about these datasets are included in the Appendix. |
| Dataset Splits | No | The paper mentions using source and target datasets but does not explicitly provide specific numerical training, validation, and test split percentages or counts for any of the datasets. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU or CPU models used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers, such as programming languages, libraries, or frameworks (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | A point of strength for our algorithm is that there are only two major algorithm-specific hyper-parameters and tuning them is not challenging. We set τ = 0.99 and λ = 10^-3. |