Overview
The upcoming NeurIPS 2019 workshop on Optimal Transport and Machine Learning (OTML’19) will take place in Vancouver, providing a platform for researchers to discuss advancements in this evolving field. This workshop aims to foster collaboration and knowledge sharing among experts in optimal transport theory and its applications in machine learning.
Background & Relevance
Optimal transport (OT) has emerged as a crucial area of research within machine learning, statistics, and optimization. It offers powerful tools for comparing probability distributions and has applications across various domains, including computer vision, natural language processing, and biology. Understanding OT can lead to significant improvements in model performance and data analysis techniques, making this workshop particularly relevant for researchers and practitioners in AI and machine learning.
Key Details
- Event: NeurIPS 2019 Workshop on Optimal Transport and Machine Learning (OTML’19)
- Dates: December 13 or 14, 2019
- Location: Vancouver
- Submission Deadline: September 15, 2019 (23:59 PDT)
- Submission Link: EasyChair Submission
- Presentation Format: Accepted works will be presented as either a poster or a talk during the workshop.
Eligibility & Participation
This workshop invites submissions from researchers working at the intersection of optimal transport theory and machine learning. It targets academics, industry professionals, and students who are exploring innovative applications and theoretical advancements in this area.
Submission or Application Guidelines
- Authors may submit original research that overlaps with previously published or submitted work, provided it offers new insights.
- Submissions should adhere to the standard NeurIPS 2019 style files, with a recommended length of 6 to 10 pages.
- All submissions must be made through the EasyChair platform; email submissions will not be accepted.
- Ensure that submissions are completed before the deadline for full consideration, with each submission reviewed by at least two reviewers.
Additional Context / Real-World Relevance
The workshop will cover a range of topics related to optimal transport, including estimation of transport couplings, generalizations of OT theory, and its application as a learning method. These discussions will highlight the significance of OT in high-dimensional applications such as word embeddings and low-dimensional applications like graphics and imaging. The insights gained from this workshop could influence future research directions and practical implementations in the field.
Conclusion
The NeurIPS 2019 workshop on Optimal Transport and Machine Learning represents an excellent opportunity for researchers to showcase their work and engage with peers. Interested participants are encouraged to submit their contributions and join the discussions that will shape the future of optimal transport in machine learning.
Category: CFP & Deadlines
Tags: optimal transport, machine learning, neurips, statistics, optimization, applications, deep learning, data science
