Notice: 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. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Learning spatiotemporal piecewise-geodesic trajectories from longitudinal manifold-valued data
Authors: Stéphanie ALLASSONNIERE, Juliette Chevallier, Stephane Oudard
NeurIPS 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on synthetic data validate this choice. The model is then applied to the metastatic renal cancer chemotherapy monitoring: we run estimations on RECIST scores of treated patients and estimate the time they escape from the treatment. Experiments highlight the role of the different parameters on the response to treatment. |
| Researcher Affiliation | Academia | Juliette Chevallier CMAP, École polytechnique EMAIL Pr Stéphane Oudard Oncology Department USPC, AP-HP, HEGP Stéphanie Allassonnière CRC, Université Paris Descartes EMAIL |
| Pseudocode | No | The paper refers to supplementary material for details about algorithmics, but does not include any pseudocode or algorithm blocks in the main text. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository. |
| Open Datasets | No | We now run our estimation algorithm on real data from HEGP. We have performed the estimation over a drove of 176 patients of the HEGP. |
| Dataset Splits | No | The paper does not provide specific train/validation/test dataset splits for reproducibility. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., GPU, CPU models, or cloud instances) used for running its experiments. |
| Software Dependencies | No | The paper mentions the use of 'MONOLIX MOdèles NOn LInéaires à effets mi Xtes' but does not provide a specific version number or other software dependencies with versions. |
| Experiment Setup | Yes | Moreover, to put the algorithm on a more realistic situation, the synthetic individual times are non-periodically spaced, individual sizes vary between 12 and 18 and the observed values are noisy (σ = 3). We present here a run with a low residual standard variation in respect to the amplitude of the trajectories and complexity of the dataset: σ = 14.50 versus max(γinit 0 , γfin 0 ) γescap 0 = 452.4. We have run the algorithm several times, with different proposal laws for the sampler (a Symmetric Random Walk Hasting-Metropolis within Gibbs one) and different priors. |