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
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.
Important dates:
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.
08:30 AM Welcome Address
08:35 AM Invited Talk by Negar Kiyavash
09:15 AM Fused Gromov-Wasserstein Alignment for Hawkes Processes (Oral presentation)
Insider Threat Detection via Hierarchical Neural Temporal Point Processes (Oral presentation)
09:45 AM Coffee Break
10:30 AM Invited Talk By Niloy Ganguly
11:10 AM Intermittent Demand Forecasting with Deep Renewal Processes (Oral presentation)
Temporal Logic Point Processes (Oral presentation)
The Graph Hawkes Network for Reasoning on Temporal Knowledge Graphs (Oral presentation)
Multivariate coupling estimation between continuous signals and point processes (Oral presentation)
12:10 PM Lunch Break
01:50 PM Invited Talk by Walter Dempsey
02:30 PM Better Approximate Inference for Partial Likelihood Models with a Latent Structure (Oral presentation)
Deep Point Process Destructors
A sleep-wake detection algorithm for memory-constrained wearable devices: Change Point Decoder (Oral presentation)
Topics are not Marks: Modeling Text-based Cascades using Multi-network Hawkes Process (Oral presentation)
03:30 PM Poster Setup + Coffee Break
04:15 PM Emerging topics in Temporal point process (Panel discussion)
05:00 PM Poster Session
06:00 PM Closing Address
Link to the papers would be updated shortly.