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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
Efficient and Consistent Adversarial Bipartite Matching
Authors: Rizal Fathony, Sima Behpour, Xinhua Zhang, Brian Ziebart
ICML 2018 | Venue PDF | LLM Run Details | Input Tokens: 19,981 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,361 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To evaluate our approach, we apply our adversarial bipartite matching model to video tracking tasks using public benchmark datasets (Leal-Taix e et al., 2015). Our future work will explore matching problems with different loss functions and other graphical structures. |
| Researcher Affiliation | Academia | 1Department of Computer Science, University of Illinois at Chicago. |
| Pseudocode | Yes | Algorithm 1 Double Oracle Algorithm for Adversarial Bipartite Matching Equilibria. |
| Open Source Code | No | The paper does not provide an explicit statement or link for the availability of its source code. |
| Open Datasets | Yes | To evaluate our approach, we apply our adversarial bipartite matching model to video tracking tasks using public benchmark datasets (Leal-Taix e et al., 2015). Table 3. Dataset properties DATASET # ELEMENTS # EXAMPLES TUD-CAMPUS 12 70 TUD-STADTMITTE 16 178 ETH-SUNNYDAY 18 353 ETH-BAHNHOF 34 999 ETH-PEDCROSS2 30 836 |
| Dataset Splits | Yes | To tune the regularization parameter (λ in adversarial matching, and C in SSVM), we perform 5-fold cross validation based on the training dataset only. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running its experiments. |
| Software Dependencies | No | The paper mentions software like "min Conf" (Schmidt, 2008) and "SVM-Struct" (Joachims, 2008; Vedaldi, 2011) but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | No | The paper mentions tuning regularization parameters but does not provide specific values for hyperparameters such as learning rate, batch size, number of epochs, or optimizer settings. |