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 Gomory-Hu Trees
Authors: Anders Aamand, Justin Chen, Mina Dalirrooyfard, Slobodan Mitrovic, Yuriy Nevmyvaka, Sandeep Silwal, Yinzhan Xu
NeurIPS 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We design a differentially private (DP) algorithm that computes an approximate Gomory-Hu tree. Our algorithm is ε-DP, runs in polynomial time, and can be used to compute s-t cuts that are O(n/ε)-additive approximations of the Min-s-t-Cuts in G for all distinct s, t V with high probability. Due to space constraints, all pseudocode and proofs are given in the appendix. This paper presents work whose goal is to advance algorithmic and theoretical tools in the field of Machine Learning. |
| Researcher Affiliation | Collaboration | Anders Aamand BARC, University of Copenhagen EMAIL Justin Y. Chen Massachusetts Institute of Technology EMAIL Mina Dalirrooyfard Morgan Stanley EMAIL Slobodan Mitrovi c UC Davis EMAIL Yuriy Nevmyvaka Morgan Stanley EMAIL Sandeep Silwal UW-Madison EMAIL Yinzhan Xu UC San Diego EMAIL |
| Pseudocode | Yes | Due to space constraints, all pseudocode and proofs are given in the appendix. Algorithm 1: Private Min Isolating Cuts(G = (V, E, w), R, U, ε, β) ... Algorithm 2: Private GHTree Step(G = (V, E, w), s, U, ε, β) ... Algorithm 3: Private GHTree(G = (V, E, w), ε) ... Algorithm 4: Private GHSteiner Tree(G = (V, E, w), U, ε, t, nmax) ... Algorithm 5: Combine((Tlarge, flarge), {(Tv, fv) : v R}, { ˆSv : v R)}) |
| Open Source Code | No | The paper does not contain any statement about code availability, nor does it provide any links to a code repository. The NeurIPS checklist question 5 has 'Answer: [NA] Justification: The paper has no experiments.', implying no code for experiments. |
| Open Datasets | No | The paper describes a theoretical framework for differentially private Gomory-Hu trees and does not involve empirical evaluation on any dataset. The NeurIPS checklist question 4 & 5 indicate no experiments. |
| Dataset Splits | No | The paper is theoretical and does not involve experimental evaluation with datasets, therefore no dataset split information is provided. |
| Hardware Specification | No | The paper is theoretical and does not involve experimental evaluation, thus no hardware specifications are provided. |
| Software Dependencies | No | The paper is theoretical and does not involve experimental evaluation, thus no specific software dependencies with version numbers are mentioned for reproducibility. |
| Experiment Setup | No | The paper is theoretical and does not involve experimental evaluation, thus no experimental setup details like hyperparameters or training configurations are provided. |