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..
Privately Learning Subspaces
Authors: Vikrant Singhal, Thomas Steinke
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We present differentially private algorithms that take input data sampled from a low-dimensional linear subspace (possibly with a small amount of error) and output that subspace (or an approximation to it). Theorem 1.1 (Main Result Exact Case). For all n, d, k, ℓ N and ε, δ > 0 satisfying n O ℓ+ log(1/δ) ε , there exists a randomized algorithm M : Rd n Sk d satisfying the following. Algorithm 1: DP Exact Subspace Estimator Algorithm 2: DP Approximate Subspace Estimator |
| Researcher Affiliation | Collaboration | Vikrant Singhal Cheriton School of Computer Science University of Waterloo Waterloo, ON N2L 3G1, Canada EMAIL Thomas Steinke Google Research, Brain Team Mountain View, CA, United States of America EMAIL |
| Pseudocode | Yes | Algorithm 1: DP Exact Subspace Estimator DPESEε,δ,k,ℓ(X) Algorithm 2: DP Approximate Subspace Estimator DPASEε,δ,α,γ,k(X) |
| Open Source Code | No | The paper does not contain any explicit statement about open-sourcing code, nor does it provide a link to a code repository. |
| Open Datasets | No | The paper describes theoretical algorithms and their mathematical properties, assuming data is sampled from a distribution (e.g., 'data comes from a Gaussian distribution'). It does not use or mention any specific public or open dataset. |
| Dataset Splits | No | The paper focuses on theoretical analysis and does not describe any experiments that would involve training, validation, or test dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any experiments, therefore no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe any software dependencies or specific version numbers for replication. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup details such as hyperparameters or training configurations. |