# Neural graphical modelling in continuous-time: consistency guarantees and algorithms

@inproceedings{Bellot2021NeuralGM, title={Neural graphical modelling in continuous-time: consistency guarantees and algorithms}, author={Alexis Bellot and Kim Branson and Mihaela van der Schaar}, booktitle={International Conference on Learning Representations}, year={2021} }

The discovery of structure from time series data is a key problem in fields of study working with complex systems. Most identifiability results and learning algorithms assume the underlying dynamics to be discrete in time. Comparatively few, in contrast, explicitly define dependencies in infinitesimal intervals of time, independently of the scale of observation and of the regularity of sampling. In this paper, we consider score-based structure learning for the study of dynamical systems. We…

## 5 Citations

### Identifiability and Asymptotics in Learning Homogeneous Linear ODE Systems from Discrete Observations

- Mathematics, Computer ScienceArXiv
- 2022

This work derives a suﬃcient condition for the identiﬁability of homogeneous linear ODE systems from a sequence of equally-spaced error-free observations sampled from a single trajectory and proposes a new method to infer the causal structure of the ODE system, i.e., inferring whether there is a causal link between system variables.

### Sparsity in Continuous-Depth Neural Networks

- Computer ScienceArXiv
- 2022

It is demonstrated that sparsity improves out-of-distribution generalization (for the types of OOD considered) of NODEs, and proposed PathReg, a regularizer acting directly on entire paths throughout a neural network and achieving exact zeros is proposed.

### Bayesian Dynamic Causal Discovery

- Computer Science
- 2022

A new framework for Bayesian causal discovery for dynamical systems is proposed and a novel generative flow network architecture (Dyn-GFN) tailored for this task is presented, which imposes an edge-wise sparse prior to sequentially build a k -sparse causal graph.

### Towards Better Long-range Time Series Forecasting using Generative Forecasting

- Computer ScienceArXiv
- 2022

A new forecasting strategy called Generative Forecasting (GenF), which generates synthetic data for the next few time steps and then makes long- range forecasts based on generated and observed data, is proposed.

### Towards Better Long-range Time Series Forecasting using Generative Adversarial Networks

- Computer ScienceArXiv
- 2021

A new forecasting strategy called Generative Forecasting (GenF), which generates synthetic data for the next few time steps and then makes long-range forecasts based on generated and observed data, and theoretically proves that GenF is able to better balance the forecasting variance and bias, leading to a much smaller forecasting error.

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