Online Control in Population Dynamics
Authors: Noah Golowich, Elad Hazan, Zhou Lu, Dhruv Rohatgi, Y. Jennifer Sun
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our empirical evaluations demonstrate the effectiveness of the proposed algorithm for population control even in non-linear models such as SIR and replicator dynamics. |
| Researcher Affiliation | Collaboration | MIT. nzg@mit.edu. :Google Deep Mind & Princeton University. ehazan@princeton.edu. ;Princeton University. zhoul@princeton.edu. MIT. drohatgi@mit.edu. Princeton University. ys7849@princeton.edu. |
| Pseudocode | Yes | Algorithm 1 GPC-Simplex: GPC for Simplex LDS |
| Open Source Code | Yes | Relevant code is open-sourced in [16]. |
| Open Datasets | No | The paper focuses on simulating dynamical systems (SIR model, replicator dynamics) rather than using pre-existing, publicly available datasets for training/testing. While the models are well-known, no concrete access information for a specific dataset is provided. |
| Dataset Splits | No | The paper does not provide specific dataset split information (e.g., percentages, sample counts) for training, validation, or testing, as it focuses on simulations of dynamical systems rather than traditional machine learning datasets. |
| Hardware Specification | No | They were run on Google Colab with default compute resources. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., specific Python, PyTorch, or TensorFlow versions used). |
| Experiment Setup | Yes | To model a highly infectious pandemic, we consider Eq. (11) with parameters β 0.5, θ 0.03, and ξ 0.005. ... ctpxt, utq c3 xtp2q2 c2 xtp1q utp1q. ... In our experiments, we always set η : a d H lnp Hq{p2 ? Tq. |