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..
Towards Optimal Effective Resistance Estimation
Authors: Rajat Vadiraj Dwaraknath, Ishani Karmarkar, Aaron Sidford
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We provide new algorithms and conditional hardness for the problem of estimating effective resistances in n-node m-edge undirected, expander graphs. Both our algorithms and lower bounds develop more general tools for handling related problems for more general (not-necessarily Laplacian) matrices. |
| Researcher Affiliation | Academia | Rajat Vadiraj Dwaraknath Stanford University EMAIL Ishani Karmarkar Stanford University EMAIL Aaron Sidford Stanford University EMAIL |
| Pseudocode | Yes | Because the ~ÎŽi,j queries appearing in effective resistance computations are 2-sparse and p2, DMqnumerically sparse for all SDD matrices M, taking M LG in Theorem 7 immediately implies Theorem 2 and Theorem 3 (see supplementary material for detailed discussion and pseudocode.) |
| Open Source Code | No | The paper describes algorithms and theoretical results, but does not state that source code for the methodology is openly available or provide a link. |
| Open Datasets | No | This is a theoretical paper focused on algorithms and lower bounds; it does not utilize datasets for empirical training or evaluation. |
| Dataset Splits | No | This is a theoretical paper; it does not involve dataset splits for training, validation, or testing. |
| Hardware Specification | No | This is a theoretical paper and does not describe hardware used for experiments. |
| Software Dependencies | No | This is a theoretical paper and does not list specific software dependencies with version numbers for experimental reproducibility. |
| Experiment Setup | No | This is a theoretical paper focused on algorithms and lower bounds, and as such, it does not include details on experimental setup or hyperparameters. |