Learning with Temporal Point Processes
In this workshop, we take a broad perspective in learning with temporal point process and address a wide range of challenges including but not limited to
- Mathematical modeling of real life phenomena using temporal point process
- Machine learning methods for inference of temporal point process.
- Control of temporal point process
- Reinforcement learning of temporal point process
- Causal learning of temporal point process
- Inference of temporal point process with intermittent observations
- Emerging applications of temporal point process e.g. epidemic modeling, deep generative models, etc.
Call for papers and important dates
In recent years, there has been an increasing number of machine learning models, inference methods and control algorithms using temporal point processes. They have been particularly popular for understanding, predicting, and enhancing the functioning of social and information systems, where they have achieved unprecedented performance. This workshop aims to introduce temporal point processes to the machine learning community at large. In this workshop, we aim to popularize temporal point processes within the machine learning community at large. It will bring together experts from a diverse, multi-disciplinary set of backgrounds. One of the main goals of this workshop is to help the community understand the role of temporal point process on the development of human-centered machine learning models and algorithms accounting for the feedback loop between algorithmic and human decisions, which are inherently asynchronous events.
We take a broad perspective to learning with temporal point process and address a wide range of themes including, but not limited to predictive models, efficient inference methods, deep learning, control and reinforcement learning, generative models, causal learning, incomplete and missing data We also encourage the submission of emerging real-world applications of TPPs.
- Submission deadline: September 15, 23:59 AOE
- Author notification: September 30
Submissions in the form of extended abstracts must be at most 4 pages long (not including references and an unlimited number of pages for supplemental material, which reviewers are not required to take into account) and adhere to the NeurIPS format. We accept submissions of work recently published or currently under review. Submissions should be anonymized as described in the submission instructions and should be submitted through:
The workshop will not have formal proceedings, but authors of accepted abstracts can choose to have either a link to an arxiv version of their paper or a pdf published on the workshop webpage.
🚧 coming soon 🚧
Planned: invited talks, contributed talks, poster session, panel discussions
- Manuel Gomez Rodriguez (Max Planck Institute for Software Systems)
- Le Song (Georgia Institute of Technology)
- Isabel Valera (Max Planck Institute for Intelligent Systems)
- Yan Liu (University of Southern California)
- Abir De (Max Planck Institute for Software Systems)
- Hongyuan Zha (Georgia Institute of Technology)