Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
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 | LLM Run Details | Input Tokens: 31,169 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,678 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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) |