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..
Multi-agent Performative Prediction with Greedy Deployment and Consensus Seeking Agents
Authors: Qiang LI, Chung-Yiu Yau, Hoi-To Wai
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical results validate our analysis. We consider two examples of performative prediction problems to verify our theories. All experiments are conducted with Python on a server using 80 threads of an Intel Xeon 6318 CPU. Multi-agent Gaussian Mean Estimation. We aim to illustrate Proposition 1, Theorem 1 via a scalar Gaussian mean estimation problem on synthetic data. Email Spam Classification. We evaluate the performance of DSGD-GD by simulating the performative effects on a real dataset. |
| Researcher Affiliation | Academia | Qiang Li Chung-Yiu Yau Hoi-To Wai Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong, Shatin, Hong Kong SAR of China EMAIL |
| Pseudocode | Yes | DSGD with Greedy Deployment (DSGD-GD) Scheme At iteration t = 0, 1, ..., for any i V , agent i updates his/her decision (θt i) by the recursion consisting of two phases (Phase 1) Zt+1 i Di(θt i) (Phase 2) θt+1 i = Pn j=1 Wijθt j γt+1 ℓ(θt i; Zt+1 i ), (5) |
| Open Source Code | No | The paper does not contain any explicit statements or links indicating that the source code for the described methodology is open-source or publicly available. |
| Open Datasets | Yes | Email Spam Classification. This example is a multi-agent spam classification task based on spambase, a dataset [Hopkins, 1999] with m = 4601 samples, d = 48 features. |
| Dataset Splits | Yes | Each server has access to training data from mi = 138 samples from spambase modeling the different set of users; the rest of mtrain = 1150 samples are taken as testing data. |
| Hardware Specification | Yes | All experiments are conducted with Python on a server using 80 threads of an Intel Xeon 6318 CPU. |
| Software Dependencies | No | The paper mentions |
| Experiment Setup | Yes | In our experiments, we set zi = 10, σ2 = 50 and step size for DSGD-GD as γt = a0/(a1 + t) with a0 = 50, a1 = 104. The sensitivity parameters are set as ϵi {0.4ϵavg, 0.45ϵavg, ..., 1.6ϵavg} with ϵ = ϵavg {0.01, 0.1, 1}. The servers aim to find a common spam filter classifier via (2) with β = 10−4. |