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.
Robust and Scalable SDE Learning: A Functional Perspective
Authors: Scott Alexander Cameron, Tyron Luke Cameron, Arnu Pretorius, Stephen J. Roberts
ICLR 2022 | Venue PDF | LLM Run Details | Input Tokens: 17,717 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,587 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 | In this section, we provide some experiments to illustrate and verify the performance and capabilities of our proposed algorithm. |
| Researcher Affiliation | Collaboration | Scott Cameron Oxford University, Insta Deep Ltd. United Kingdom EMAIL Tyron Cameron Discovery Insure South Africa Arnu Pretorius Insta Deep Ltd. South Africa Stephen Roberts Oxford University United Kingdom |
| Pseudocode | Yes | Algorithm 1 Path Integral Importance Sampling; Algorithm 2 Transformed-State Path Integral Importance Sampling |
| Open Source Code | Yes | The code included in the supplementary material includes a Dockerfile and instructions for running some of the experiments. |
| Open Datasets | Yes | The data set we used was the Hungarian chickenpox cases dataset from the UCI machine learning repository; it has 20 features per observation. This dataset can be found at https://archive.ics.uci.edu/ml/datasets/Hungarian+Chickenpox+Cases |
| Dataset Splits | No | The paper uses '16 independent paths' and 'observations' for training and evaluation metrics, but it does not specify explicit train/validation/test dataset splits (e.g., percentages or counts for distinct sets). |
| Hardware Specification | Yes | These models were trained on an Nvidia RTX 3070, with 8Gb of memory. |
| Software Dependencies | No | The paper mentions using the 'Adam optimizer' and 'neural network' architectures, but does not provide specific version numbers for software libraries, frameworks (e.g., PyTorch, TensorFlow), or programming languages. |
| Experiment Setup | Yes | For each model we used the Adam optimizer with a learning rate of 10-3 and ran the optimization algorithm for 104 iterations. In both cases we used K = 64 and a time step size of t = 10-2. |