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 [1].
Tackling Stackelberg Network Interdiction against a Boundedly Rational Adversary
Authors: Tien Mai, Avinandan Bose, Arunesh Sinha, Thanh Nguyen, Ayushman Kumar singh
IJCAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To illustrate the efficacy of our proposed algorithms, we perform experiments on synthetic data. |
| Researcher Affiliation | Academia | 1Singapore Management University 2University of Washington 3Rutgers University 4University of Oregon 5Indian Institute of Technology, Delhi EMAIL, EMAIL, EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: Dynamic Programming based algorithm (Dyn P) to solve Maximizing g(x, λ) |
| Open Source Code | No | The paper does not provide concrete access to source code (specific repository link, explicit code release statement, or code in supplementary materials) for the methodology described. |
| Open Datasets | No | The paper uses "synthetic data" but does not provide concrete access information for a publicly available or open dataset, nor does it cite a dataset that is publicly available. |
| Dataset Splits | No | The paper mentions generating random graphs and varying the number of nodes, but does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning. |
| Hardware Specification | Yes | All our experiments were run on a 2.1 GHz CPU with 128GB RAM. |
| Software Dependencies | Yes | We use GUROBI (a SOTA MILP solver) to solve (MILP). |
| Experiment Setup | Yes | Moreover, we used p = 0.8, µ = 2. ... To sample paths for the baseline, a resource allocation x is assigned to L and the follower is initially placed at the origin so. Its next node s1 is sampled from the distribution ... This sampling is repeated 1000 times per iteration and then average is taken to get the objective. Based on the gradients, the resource allocation x is updated which changes the transition probabilities and the process is repeated again until convergence. Ten different values of x were taken and the seed with the lowest loss was reported. ... For each value of K, we generate 10 independent instances and solve them using the MILP approach. ... We therefore choose N = 90 and K =20 for the Li SD approach. |