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 Framework for Private Matrix Analysis in Sliding Window Model
Authors: Jalaj Upadhyay, Sarvagya Upadhyay
ICML 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We perform a rigorous study of private matrix analysis when only the last W updates to matrices are considered useful for analysis. We show the existing framework in the non-private setting is not robust to noise required for privacy. We then propose a framework robust to noise and use it to give first efficient o(W) space differentially private algorithms for spectral approximation, principal component analysis (PCA), multi-response linear regression, sparse PCA, and non-negative PCA. Prior to our work, no such result was known for sparse and non-negative differentially private PCA even in the static data setting. We also give a lower bound to demonstrate the cost of privacy. |
| Researcher Affiliation | Industry | 1Apple, USA (work done when the author was between jobs). 2Fujitsu Research of America, USA. Correspondence to: Jalaj Upadhyay <EMAIL>. |
| Pseudocode | Yes | The maintenance phase (high-level description of this phase is provided in Algorithm 1) ensures that the final set of matrices satisfies -approximate spectral histogram property. |
| Open Source Code | No | The paper does not provide a specific repository link, explicit code release statement, or mention of code in supplementary materials for the methodology described. |
| Open Datasets | No | The paper is theoretical and does not conduct empirical studies using datasets, hence no information about public datasets is provided. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical validation with datasets, therefore it does not provide information about training, validation, or test dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe experiments that would require specific hardware. No hardware specifications are provided. |
| Software Dependencies | No | The paper is theoretical and focuses on algorithm design and theoretical properties, not implementation details or specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe practical experimental setups, hyperparameter values, or training configurations. |