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..
Efficient Private Empirical Risk Minimization for High-dimensional Learning
Authors: Shiva Prasad Kasiviswanathan, Hongxia Jin
ICML 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we theoretically study the problem of differentially private empirical risk minimization in the projected subspace (compressed domain). |
| Researcher Affiliation | Industry | Shiva Prasad Kasiviswanathan EMAIL Samsung Research America, Mountain View, CA 94043 Hongxia Jin EMAIL Samsung Research America, Mountain View, CA 94043 |
| Pseudocode | Yes | Mechanism 1 PROJERM: Input: A random subgaussian matrix Φ Rm d, and a dataset D = (Φx1, y1), . . . , (Φxn, yn) of n datapoints from the domain MΦ = {(Φx, y) : x Rd, x 1, y R, |y| 1} Output: θpriv a differentially private estimate of ˆθ argminθ C 1 n Pn i=1 ℓ( xi, θ ; yi) 1. Let ϑpriv Output of an (ϵ, δ)-differentially private or an ϵ-differentially private ERM algorithm solving the following problem: argminϑ ΦC 1 n i=1 ℓ( Φxi, ϑ ; yi) 2. θpriv argminθ Rd θ C subject to Φθ = ϑpriv (can be solved with any convex programming technique) 3. Return: θpriv |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository. |
| Open Datasets | No | The paper is theoretical and discusses abstract datasets with 'n datapoints' without referring to any specific, publicly available or open datasets for training. |
| Dataset Splits | No | The paper is theoretical and does not describe experimental setups, hence no dataset split information (train/validation/test) is provided. |
| Hardware Specification | No | The paper is theoretical and does not describe any experimental setup or the hardware used for it. |
| Software Dependencies | No | The paper is theoretical and does not describe any specific software implementations or their version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup details such as hyperparameters or training configurations. |