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..
Learning privately from multiparty data
Authors: Jihun Hamm, Yingjun Cao, Mikhail Belkin
ICML 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the performance of our method with realistic tasks of activity recognition, network intrusion detection, and malicious URL detection. and We evaluate the methods with real tasks in Sec. 6 and conclude the paper in Sec. 7. |
| Researcher Affiliation | Academia | Jihun Hamm EMAIL Dept. Computer Science and Engineering, Ohio State University, Columbus, OH 43210, USA and Paul Cao EMAIL Dept. Computer Science and Engineering, UC-San Diego, La Jolla, CA 92093, USA and Mikhail Belkin EMAIL Dept. Computer Science and Engineering, Ohio State University, Columbus, OH 43210, USA |
| Pseudocode | Yes | Algorithm 1 DP Ensemble by Majority-voted ERM and Algorithm 2 DP Ensemble by Weighted ERM |
| Open Source Code | No | The paper does not provide any statement or link indicating the availability of open-source code for the methodology described. |
| Open Datasets | Yes | We use the UCI Human Activity Recognition Dataset (Anguita et al., 2012), which is a collection of motion sensor data on a smart device by multiple subjects performing 6 activities... and For this task we use the KDD-99 dataset, which consists of examples of bad connections... and The Malicious URL Dataset (Ma et al., 2009) is a collection of examples of malicious URLs... |
| Dataset Splits | No | The paper specifies training and testing sample sizes (e.g., 'The training and testing samples are 7K and 3K, respectively.' for Activity Recognition) but does not explicitly mention a separate validation split or how model selection was performed using validation data. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., library names, programming language versions, or specific solver versions) used in the experiments. |
| Experiment Setup | Yes | The only hyperparameter of the model is the regularization coefficient λ which we fixed to 10 4 after performing some preliminary experiments. |