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..
Differentially Private Correlation Alignment for Domain Adaptation
Authors: Kaizhong Jin, Xiang Cheng, Jiaxi Yang, Kaiyuan Shen
IJCAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on standard benchmark datasets confirm the effectiveness of our approach. |
| Researcher Affiliation | Academia | State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not include any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | We evaluate our approach on two popular domain adaptation benchmark datasets. The first one is Office-Caltech10 dataset [Gong et al., 2012]... The second one is Amazon review dataset [Blitzer et al., 2006]... |
| Dataset Splits | No | The paper defines domain adaptation tasks (e.g., A D (train on A, test on D)) but does not specify train/validation/test splits within these domains (e.g., percentages or sample counts for each subset). |
| Hardware Specification | No | The paper does not specify the hardware (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies or libraries used in the experiments. |
| Experiment Setup | Yes | There are 5 prime parameters in PRIMA. Among them, ϵ, δ, σ are privacy parameters, batch size b, clipping bound c are model training parameters. We follow the experimental protocol used in [Abadi et al., 2016] by setting σ = 4, δ = 10 5, and compute the value of ϵ as a function of the training epochs E. We follow the experimental protocol of [Abadi et al., 2016] again by setting c as the median of the unclipped gradients over the course of training. Empirically, batch size b is set to 25. |