Network Effects in Performative Prediction Games
Authors: Xiaolu Wang, Chung-Yiu Yau, Hoi To Wai
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical illustrations on the network effects in Multi-PP games corroborate our findings. Numerical Illustration. We examine the network effects on the multi-agent logistic regression game via simulating the SG-GD algorithm. |
| Researcher Affiliation | Academia | 1Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong, Hong Kong, China. |
| Pseudocode | Yes | Algorithm 1 Stochastic Gradient Greedy Deployment 1: Input: θ0 i for i [n], step size γt > 0 for t 1. 2: for t = 0, 1, . . . do 3: Deploy the models {θt i}n i=1 at the population. 4: for i = 1 to n do {executed in parallel} 5: Sample Zt+1 i Di(θt i, θt Ni) 6: Set gt = ℓi(θt i; Zt+1 i )+ρi Pn j=1 Aij θt i θt j 7: Set θt+1 i = θt i γt+1gt 8: end for 9: end for |
| Open Source Code | No | No statement about open-source code release found. |
| Open Datasets | Yes | Finally, we validate the results of this paper in a semirealistic setting by sampling from a Kaggle dataset (Give Me Some Credit). |
| Dataset Splits | No | Similar to Bellet et al. (2018), each agent holds a training dataset of size 1 Si 100 and a testing dataset of size 100. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory) used for experiments were mentioned. |
| Software Dependencies | No | No specific software dependencies with version numbers were mentioned. |
| Experiment Setup | Yes | From Figure 4, we observe that while enabling graph regularization (with ρ = 1) allows the agents to maintain a high accuracy in classification in general ( ε {0, 0.1}), under large distribution shifts ( ε = 10) of negative samples, it may lead to degraded performance. logistic regression problem with an ℓ2-regularization λ2 θi 2 and λ = 10 4. |