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.
Black-Box Variational Inference for Stochastic Differential Equations
Authors: Tom Ryder, Andrew Golightly, A. Stephen McGough, Dennis Prangle
ICML 2018 | Venue PDF | LLM Run Details | Input Tokens: 17,057 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,262 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 illustrate the method on a Lotka Volterra system and an epidemic model, producing accurate parameter estimates in a few hours. We implement our method for two examples: (1) analysing synthetic data from a Lotka-Volterra SDE; (2) analysing real data from an SDE model of a susceptible-infectiousremoved (SIR) epidemic. Our experiments include challenging regimes such as: (A) low-variance observations; (B) conditioned diffusions with non-linear dynamics; (C) unobserved time series; (D) widely spaced observation times; (E) data which is highly unlikely under the unconditioned model. In all our experiments below similar tuning choices worked well. We use batch size n = 50 in (22). Our RNN cell has four hidden layers each with 20 hidden units and rectified-linear activation. We implement our algorithms in TensorFlow using the Adam optimiser (Kingma & Ba, 2015) and report results using an 8-core CPU. |
| Researcher Affiliation | Academia | 1School of Mathematics, Statistics and Physics, Newcastle University, Newcastle, UK 2School of Computing, Newcastle University, Newcastle, UK. |
| Pseudocode | Yes | Algorithm 1 Black-box variational inference for SDEs |
| Open Source Code | Yes | The code is available at https://github.com/Tom-Ryder/VIforSDEs. |
| Open Datasets | Yes | Our data is taken from an outbreak of influenza at a boys boarding school in 1978 (Jackson et al., 2013). |
| Dataset Splits | No | The paper does not explicitly provide training, validation, and test dataset splits needed to reproduce the experiment. It discusses batch size and generating synthetic data, but not how data was partitioned for training, validation, and testing. |
| Hardware Specification | Yes | We implement our algorithms in TensorFlow using the Adam optimiser (Kingma & Ba, 2015) and report results using an 8-core CPU. |
| Software Dependencies | No | The paper mentions using TensorFlow and the Adam optimizer, but it does not specify version numbers for these software components. |
| Experiment Setup | Yes | We use batch size n = 50 in (22). Our RNN cell has four hidden layers each with 20 hidden units and rectified-linear activation. We implement our algorithms in TensorFlow using the Adam optimiser (Kingma & Ba, 2015). |