A Probabilistic Covariate Shift Assumption for Domain Adaptation
Authors: Tameem Adel, Alexander Wong
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | 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 thesham@uwaterloo.ca Alexander Wong University of Waterloo a28wong@uwaterloo.ca |
| 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). |