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..
Private Zeroth-Order Nonsmooth Nonconvex Optimization
Authors: Qinzi Zhang, Hoang Tran, Ashok Cutkosky
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We introduce a new zeroth-order algorithm for private stochastic optimization on nonconvex and nonsmooth objectives. Our algorithm satisfies (α, αρ2/2)-R enyi differential privacy (RDP) (Mironov, 2017) (which is approximately (ρ, γ)-DP) and finds a (δ, ϵ)-stationary point with O(dδ 1ϵ 3+d3/2ρ 1δ 1ϵ 2) data complexity. This paper presents a novel zeroth-order algorithm for private nonsmooth nonconvex optimization. |
| Researcher Affiliation | Academia | Qinzi Zhang, Hoang Tran & Ashok Cutkosky Department of Electrical and Computer Engineering Boston University Boston, MA, USA EMAIL |
| Pseudocode | Yes | Algorithm 1 Zeroth-order gradient oracle GRADf,δ(x, z1:b), Algorithm 2 Zeroth-order gradient difference oracle DIFFf,δ(x, y, z1:b), Algorithm 3 Online-to-Nonconvex Conversion, Algorithm 4 Private variance-reduced gradient oracle O, Algorithm 5 Tree Mechanism |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described, nor does it include specific repository links or explicit code release statements. |
| Open Datasets | No | The paper focuses on theoretical analysis and algorithm design and does not describe experiments using datasets. |
| Dataset Splits | No | The paper is theoretical and does not describe experiments with dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe experiments, therefore no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe experiments, therefore no specific ancillary software dependencies with version numbers are mentioned. |
| Experiment Setup | No | The paper focuses on theoretical analysis and algorithm design, and does not provide specific experimental setup details such as hyperparameters or training configurations. |