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..
Audit Games with Multiple Defender Resources
Authors: Jeremiah Blocki, Nicolas Christin, Anupam Datta, Ariel Procaccia, Arunesh Sinha
AAAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In addition, we experimentally demonstrate that this transformation significantly speeds up computation of solutions for a class of audit games and security games. In this section, we empirically demonstrate the speedup gains from our optimization transformation for both audit games and security games. |
| Researcher Affiliation | Academia | 1Carnegie Mellon University, USA; {arielpro@cs., jblocki@cs., danupam@, nicolasc@}cmu.edu 2University of Southern California, USA; EMAIL |
| Pseudocode | Yes | Algorithm 1: CONSTRAINT FIND(T, R) and Algorithm 2: APX SOLVE(l, EQ(j)) |
| Open Source Code | No | The paper states 'Code was written in Matlab using the built-in large scale interior point method implementation of linear programming' but does not provide a concrete link to or statement about the public availability of their source code for the methodology described. |
| Open Datasets | No | The paper states that 'utilities were generated randomly from the range [0, 1]' for their experiments, indicating synthetic data, and does not provide access information for a publicly available or open dataset. |
| Dataset Splits | No | The paper does not specify explicit training, validation, or test dataset splits. It mentions 'random utilities' and running experiments for '5 runs' but no detailed split information for reproducibility. |
| Hardware Specification | Yes | Our experiments were run on a desktop with quad core 3.2 GHz processor and 6GB RAM. |
| Software Dependencies | No | The paper states 'Code was written in Matlab using the built-in large scale interior point method implementation of linear programming.' However, it does not provide specific version numbers for Matlab or any particular libraries/solvers used. |
| Experiment Setup | Yes | We used the same problem inputs in which utilities were generated randomly from the range [0, 1], a was fixed to 0.01, x was discretized with interval size of 0.005. |