Overview
The 2nd Symposium on Advances in Approximate Bayesian Inference invites researchers in machine learning and statistics to share their latest findings. Scheduled for December 8, 2019, in Vancouver, Canada, this symposium aims to foster discussions on the advancements in approximate Bayesian inference, a critical area in the field of AI and statistics.
Background & Relevance
Approximate Bayesian inference has gained significant traction in recent years, enabling effective Bayesian analysis in complex scenarios involving intricate probabilistic models and large datasets. This symposium will explore the implications of these advancements, particularly in relation to variational and Monte Carlo methods. The event encourages contributions that link Bayesian inference with various domains, including reinforcement learning and causal inference, highlighting its relevance in contemporary research.
Key Details
- Event: 2nd Symposium on Advances in Approximate Bayesian Inference
- Date: December 8, 2019
- Location: Vancouver, Canada
- Submission Deadline: October 11, 2019, by 23:59 GMT
- Acceptance Notification: November 8, 2019
- Final Paper Submission: December 5, 2019
- Submission Link: OpenReview
- Template: PMLR Template
Eligibility & Participation
This call for participation is open to researchers and practitioners in the fields of machine learning and statistics. It targets those who have made advancements in approximate Bayesian inference and wish to present their work to a broader audience.
Submission or Application Guidelines
Interested participants should submit an extended abstract of 2-4 pages in PDF format, adhering to the PMLR one-column style. The review process will be double-blind, requiring authors to anonymize their submissions. Authors are encouraged to extend previous work if applicable, and supplementary material may be included, although reviewers will not be responsible for its evaluation. All submissions must be made through OpenReview by the specified deadline.
Additional Context / Real-World Relevance
The symposium serves as a platform for researchers to discuss the latest trends and methodologies in approximate Bayesian inference. By connecting various inference methods and exploring their applications in diverse fields, the event plays a crucial role in advancing knowledge and fostering collaboration within the AI and statistics communities.
Conclusion
This symposium presents a valuable opportunity for researchers to engage with peers, share insights, and contribute to the evolving landscape of approximate Bayesian inference. Interested individuals are encouraged to submit their work and participate in this significant event.
Category: CFP & Deadlines
Tags: approximate-bayesian-inference, bayesian-methods, machine-learning, statistics, reinforcement-learning, causal-inference, differential-privacy, pmlr, nips
