Mission Statement
The non-profit corporation that runs MLSys aims to foster the exchange of research advances at the intersection of machine learning and systems, principally by hosting an annual interdisciplinary academic conference with the highest ethical standards for a diverse and inclusive community.
Schedule (Incomplete)
About the Conference
The MLSys research community is rapidly growing, both in academia and industry. This area historically has not been particularly well addressed by existing Machine Learning or Systems focused conferences.
To bridge this gap, we have created a new conference for research spanning ML and systems called MLSys. The goal is to make a home for exceptional research by creating a venue to showcase the work that occurs at the intersection of systems and machine learning and a community with expertise in both areas to evaluate the work.
The steering committee and program committees consist of 50 leading members of emerging machine learning systems area coming from industry and academia with expertise ranging from machine learning to systems to security.
Conference Overview
The Conference on Machine Learning and Systems targets research at the intersection of machine learning and systems. The conference aims to elicit new connections amongst these fields, including identifying best practices and design principles for learning systems, as well as developing novel learning methods and theory tailored to practical machine learning workflows. Topics include:
| - Efficient model training, inference, and serving | - Distributed and parallel learning algorithms |
| - Privacy and security for ML applications | - Testing, debugging, and monitoring of ML applications |
| - Fairness, interpretability and explainability for ML applications | - Data preparation, feature selection, and feature extraction |
| - ML programming models and abstractions | - ML compilers and runtime |
| - Programming languages for machine learning | - Visualization of data, models, and predictions |
| - Specialized hardware for machine learning | - Hardware-efficient ML methods |
| - Machine Learning for Systems | - Systems for Machine Learning |
Important Dates
| Conference Sessions | Mon Apr 11th through Thu the 14th |
| Paper Submission deadline (extended from the 8th) | Oct 15 '21 08:00 PM UTC * | |
| Paper Decision Notification | Jan 15 '22 07:00 AM UTC * | |
| Registration Opens | Feb 06 '22 09:00 PM UTC * | |
| Early pricing before this date. | Feb 28 '22 09:00 PM UTC * | |
| CameraReadyDeadline | Mar 04 '22 09:00 PM UTC * | |
| Sponsor Payment Deadline | Mar 19 '22 01:00 AM UTC * | |
| Last chance for a refund on registration fees | Apr 04 '22 08:00 PM UTC * | |
| Registration Close | Apr 15 '22 08:00 PM UTC * | |
| All dates » | Timezone: » |
Sponsors
We would like to thank our (growing) list of sponsors for their strong support for the MLSys Community. If your company would like to sponsor, please let us know.
Become a 2022 Sponsor »(not currently taking applications)Organizing Committee
|
General Chair Diana Marculescu, UT Austin Program Chairs Yuejie Chi, CMU Workshop and Tutorial Chairs Chris De Sa, Cornell Workflow Chair Boyue Li, CMU |
Sponsor Chair Wen-ming Ye, Amazon Publications Chair Jakub Konečný, Google Artifact Evaluation Chairs Tianqi Chen, CMU
|
The organizers can be contacted here.
Board
Ameet Talwalkar (President)
Virginia Smith (Secretary)
Inderjit Dhillon (Treasurer)
Matei Zaharia
Dimitris Papailiopoulos
Vivienne Sze
Alex Dimakis
Alex Smola
Ion Stoica
Steering Committee
Jennifer Chayes
Bill Dally
Jeff Dean
Michael I. Jordan
Yann LeCun
Fei-Fei Li
Alex Smola
Dawn Song
Eric Xing