System-Aware Neural ODE Processes for Few-Shot Bayesian Optimization
Authors: Jixiang Qing, Rebecca D. Langdon, Robert Matthew Lee, Behrang Shafei, Mark van der Wilk, Calvin Tsay, Ruth Misener
TMLR 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments showcasing SANODEP s potential for few-shot BO within dynamical systems. This section conducts experiments on meta-learning and few-shot BO for dynamical systems. |
| Researcher Affiliation | Collaboration | Jixiang Qing1 , Becky D Langdon1, Robert M Lee2, Behrang Shafei2, Mark van der Wilk3, Calvin Tsay1, Ruth Misener1 1Imperial College London 2BASF SE 3University of Oxford |
| Pseudocode | Yes | Algorithm 1 Learning and Inference in System Aware Neural ODE Processes (SANODEP) Algorithm 2 Model Assisted Ordinary Differential Equation Optimization Framework |
| Open Source Code | Yes | All models are implemented using Flax (Heek et al., 2023) and are open source, available in: https://github.com/ Tsing QAQ/SANODEP. |
| Open Datasets | No | Following Norcliffe et al. (2021), we treat F as a parametric function of a specific kinetic model with stochasticity induced by model parameter distributions P . (Section 6.1) D.1 Meta Training Data Definition (Appendix D.1 describes generating data for various ODE systems based on sampled parameters, not using pre-existing public datasets). |
| Dataset Splits | Yes | Excluding GP, each model was evaluated on 104 random systems, each consisting of 100 trajectories to predict in a minibatch fashion. As it is computationally infeasible to evaluate GPs on the same scale, we used a random subset of the test set, 5,000 trajectories. |
| Hardware Specification | Yes | We measure run time on the Lotka-Voterra (d = 2) problem using an NVIDIA A40 GPU |
| Software Dependencies | Yes | All models are implemented using Flax (Heek et al., 2023) and are open source, available in: https://github.com/ Tsing QAQ/SANODEP. The optimization framework is based on Trieste (Picheny et al., 2023). ODE solver: Dopri5 with rtol = 1e 5 and atol = 1e 5. We utilize trust region-based constraint optimization available in Scipy. The implementation also utilizes the parametric sampling approach of the GPJax (Pinder & Dodd, 2022) library. |
| Experiment Setup | Yes | In all of our subsequent experiments, we use Mmin = 0, Mmax = 10, Nx0 = 100, Nsys = 20, Ngrid = 100, mmin = 1, mmax = 10, nmin = 0, nmax = 45. (Appendix B.2) Model Hyperparameters: Encoder ϕr output dimension r: 50. ODE solver: Dopri5 with rtol = 1e 5 and atol = 1e 5. (Appendix A.2) |