Sixth Conference on Machine Learning and Systems ·
Southern Florida (TBD)
Sunday June 4th - Thursday June 8th, 2023

Registration 

Registration information will appear here in early spring 2023.

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 2023 Sponsor »(not currently taking applications)

Important Dates

Conference Sessions Sun Jun 4th through Thu the 8th
PaperSubmissionDeadline Oct 28 '22 07:00 PM EDT *
Paper Review Assignment To PC Nov 07 '22 03:00 PM EST *
PaperReviewsDue Jan 06 '23 03:00 AM EST *
AuthorFeedbackBegins Jan 16 '23 03:00 PM EST *
AuthorFeedbackEnds Jan 20 '23 03:00 PM EST *
ReviewerOnlineDiscussionStarts Jan 23 '23 03:00 PM EST *
ReviewerOnlineDiscussionEnds Feb 03 '23 03:00 PM EST *
Paper Decision Notification Feb 17 '23 03:00 AM EST *
CameraReadyDeadline Mar 03 '23 03:00 PM EST *
All dates »

Timezone: »

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
  • Privacy and security for ML applications
  • Fairness, interpretability and explainability for ML applications
  • ML programming models and abstractions
  • Programming languages for machine learning
  • Specialized hardware for machine learning
  • Machine Learning for Systems
  • Distributed and parallel learning algorithms
  • Testing, debugging, and monitoring of ML applications
  • Data preparation, feature selection, and feature extraction
  • ML compilers and runtime
  • Visualization of data, models, and predictions
  • Hardware-efficient ML methods
  • Systems for Machine Learning

2023 Organizers

(page not yet complete)

Committees

General Chair
Dawn Song (UC Berkeley)
Workshop and Tutorial Chairs
Beidi Chen (FAIR/CMU)
Gennady Pekhimenko (University of Toronto)
Artifact Evaluation Chairs
Hui Guan (University of Massachusetts, Amherst)
Zhihao Jia (CMU)
Panel and Young Professional Activities Chair
Udit Gupta (Harvard University)
Program Chairs
Michael Carbin (MIT)
Tianqi Chen (CMU)

2023 MLSys

Board

Ameet Talwalkar (President)
Inderjit Dhillon (Treasurer)
Dimitris Papailiopoulos 
Vivienne Sze
Alex Dimakis
Alex Smola
Ion Stoica
Diana Marculescu
Carole-Jean Wu
Yuejie Chi

Steering Committee

Jennifer Chayes 
Bill Dally 
Jeff Dean 
Michael I. Jordan 
Yann LeCun 
Fei-Fei Li 
Alex Smola 
Dawn Song 
Eric Xing

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.

 

About the Conference 

The MLSys community recognized that many critical future challenges are at the intersection of Machine Learning and Systems. The community was created to solve these exciting problems by recognizing the needs for scaling interdisciplinary collaboration as well as the importance of working together between industry and academia.

The community has come together to create a new conference for research spanning ML and systems, MLSys. The goal is to make a home for exceptional new research, a venue to showcase the growing trend, and more importantly a way to scale ever more complex research collaboration.

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. The MLSys community welcomes industry participation and sponsorships, we believe the investment will pay dividend in both technology advancement and industry growth for years to come.