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 Robust Low-Rank Approximation
Authors: Raman Arora, Vladimir braverman, Jalaj Upadhyay
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We give the first time and space-efficient differentially private algorithm for low-rank matrix approximation with respect to entrywise p-norm. and Proofs of all results are deferred to the supplementary material of this paper. and Our main result is as follows. Theorem 10. Algorithm ROBUST-LRA (see Algorithm 1) is (", δ)-differentially private. Furthermore, given a matrix A 2 Rn d, it runs in poly(k, n, d) time, e O(k(n + d)) space, and outputs a rank k matrix M such that, with probability 9/10 over the randomness of the algorithm... |
| Researcher Affiliation | Academia | Raman Arora Johns Hopkins University Baltimore, MD-21201 EMAIL Vladimir Braverman Johns Hopkins University Baltimore, MD-21201 EMAIL Jalaj Upadhyay Johns Hopkins University Baltimore, MD-21201 EMAIL |
| Pseudocode | Yes | Algorithm 1 ROBUST-LRA Input: Input data matrix A 2 Rn d, target rank k Output: Rank-k matrix M 2 Rn d and Algorithm 2 ROBUST-PCA Input: Input data matrix A 2 Rd n, target rank k Output: Rank-k projection matrix 2 Rd d |
| Open Source Code | No | The paper does not provide information about open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not mention specific datasets or their public availability for training. |
| Dataset Splits | No | The paper is theoretical and does not provide specific dataset split information. |
| Hardware Specification | No | The paper is theoretical and does not describe any hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not mention specific ancillary software details with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not provide specific experimental setup details. |