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..
A Probabilistic Covariate Shift Assumption for Domain Adaptation
Authors: Tameem Adel, Alexander Wong
AAAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our proposed algorithm on a benchmark sentiment analysis (and domain adaptation) dataset, where state-of-the-art adaptation results are achieved. |
| Researcher Affiliation | Academia | Tameem Adel University of Waterloo EMAIL Alexander Wong University of Waterloo EMAIL |
| Pseudocode | No | The paper describes the algorithm and its steps using text and mathematical formulations but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any explicit statements or links indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | To evaluate the performance of the proposed algorithm, we run our experiments on the Amazon reviews dataset (Blitzer, Dredze, and Pereira 2007). |
| Dataset Splits | No | The paper mentions 'Parameter values are determined by 10-fold cross-validation (K = 7 for Prob CS),' which is a validation strategy. However, it does not specify explicit fixed training, validation, and test dataset splits by percentages, absolute counts, or specific predefined splits for the main experimental evaluation. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU models, GPU models, memory, or cloud computing specifications) used to run the experiments. |
| Software Dependencies | No | The paper mentions using a 'linear SVM learner' and 'sequential minimal optimization (SMO)', and states that for comparison, 'CODA, SDA and m SDA, we used the implementation provided by the authors.' However, it does not provide specific version numbers for its own software dependencies, such as Python libraries or frameworks. |
| Experiment Setup | Yes | We train a linear SVM on S, the labeled 2000 instances (reviews) of a source domain. [...] It is a linear SVM learner with C = 1. [...] we select the 5,000 most frequent features (vocabulary terms of unigrams and bigrams). [...] For each instance xi in T, set the corresponding υ(ψ) according to fsvm(xi). Note that for xi not within the SVM margin, υ(ψ) = 0, thus Pr(l S(xi) = l T (xi)) = 0 and they retain their source labels. [...] Parameter values are determined by 10-fold cross-validation (K = 7 for Prob CS). |