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 Domain Adaptation with Theoretical Guarantees
Authors: Raef Bassily, Corinna Cortes, Anqi Mao, Mehryar Mohri
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | While our main objective is a theoretical analysis, we also report the results of several experiments. We first show that the non-private versions of our algorithms match state-of-the-art performance in supervised adaptation and that for larger values of the target sample size or ε, the performance of our private algorithms remains close to that of their non-private counterparts. |
| Researcher Affiliation | Collaboration | Raef Bassily 1 Corinna Cortes 2 Anqi Mao 3 Mehryar Mohri 2 3 1The Ohio State University 2Google Research, New York, NY; 3Courant Institute of Mathematical Sciences, New York, NY. Correspondence to: Anqi Mao <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Cnvx Adap Private adaptation algorithm based on F |
| Open Source Code | No | The paper does not contain any explicit statement about releasing source code or a link to a code repository. |
| Open Datasets | Yes | We consider five regression datasets with dimensions as high as 384 from the UCI machine learning repository (Dua & Graff, 2017), the Wind, Airline, Gas, News and Slice. |
| Dataset Splits | Yes | We carry out model selection on the target validation set and report in Table 1 the mean and standard deviation on the test set over 10 random splits of the target training and validation sets. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models or processor types used for running its experiments. |
| Software Dependencies | No | The paper mentions using logistic regression classifiers but does not specify any software dependencies with version numbers. |
| Experiment Setup | Yes | For details on hyperparameter tuning see Appendix E. |