Influence in Classification via Cooperative Game Theory
Authors: Amit Datta, Anupam Datta, Ariel D. Procaccia, Yair Zick
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We complement our theoretical results with an implementation of our approach, which serves as a proof of concept (Section 5). Using our framework, we identify ads where certain user features have a significant influence on whether the ad is shown to users. Our experiments show that our influence measures behave in a desirable manner. |
| Researcher Affiliation | Academia | Amit Datta and Anupam Datta and Ariel D. Procaccia and Yair Zick Carnegie Mellon University amitdatta,danupam@cmu.edu arielpro,yairzick@cs.cmu.edu |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. It mentions using a third-party tool ('Ad Fisher') but does not state that the authors' own implementation is open-source or provide a link. |
| Open Datasets | No | The paper describes collecting its own experimental data ("The 1200 browsers received a total of 32, 451 ads (763 unique)...") but does not provide concrete access information (link, DOI, or formal citation with authors/year) for a publicly available or open dataset. |
| Dataset Splits | No | The paper describes data collection but does not provide specific dataset split information (exact percentages, sample counts, or detailed splitting methodology) for training, validation, or testing. |
| Hardware Specification | No | The paper mentions that 'all browser instances were run from the same stationary Ubuntu machine' but does not provide specific hardware details like exact CPU/GPU models, processor types, or memory amounts. |
| Software Dependencies | No | The paper mentions running experiments on an 'Ubuntu machine' and using the 'Ad Fisher tool', but does not provide specific ancillary software details, such as library or solver names with version numbers. |
| Experiment Setup | Yes | We pick the set of features: N = {gender, age, language}. Feature states are {male, female} for gender, {18 24, 35 44, 55 64} for age, and {English, Spanish} for language; this gives us 2 3 2 = 12 possible user profiles. Using Ad Fisher, we launch twelve fresh browser instances, and assign each one a random user profile. For each browser, the news page is reloaded 10 times with 5 second intervals. To eliminate ads differing due to random chance, we collect ads over 100 iterations, each comprising of 12 browser instances, thereby obtaining data for 1200 simulated users. The pseudo-distance we use is Cosine similarity. |