Third Conference on Machine Learning and Systems · Austin
Mon Mar 2nd through Wed the 4th Visit Austin »












Archival Video

(these are the only videos from MLSys 2020)

Christopher Ré (Stanford University): Theory & Systems for Weak Supervision

Shafi Goldwasser (Simons Institute,UC Berkeley): Cryptography for Safe Machine Learning

 

Announcements

Conference Brochure »

  • Deadline for full refund extended to Sunday, March 1st at 4pm PST. The conference has NOT been canceled or posponed.
  • Early pricing extended to Feb 17th to accommodate workshop presenters
  • The 2020 conference will have a workshop track following the main conference on Mar 4. Workshops are included in the ticket price.
  • Due to trademark concerns, the Conference on Systems and Machine Learning (SysML) has changed its name to the Conference on Machine Learning and Systems (MLSys)

Board

Ameet Talwalkar (President)
Virginia Smith (Secretary)
Inderjit Dhillon (Treasurer)
Matei Zaharia

Steering Committee

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

 

Organizing Committee

General Chair

Inderjit Dhillon

Program Chairs

Dimitris Papailiopoulos
Vivienne Sze

Workshop Chairs

Ralf Herbrich
Theodoros Rekatsinas

Publications Chair

Jakub Konečný

Artifact Evaluation Chairs

Grigori Fursin
Gennady Pekhimenko

Publicity Chair

Gauri Joshi

Demo Chairs

Jing Li
Ce Zhang

Web Chair

Siddhartha Sen

The organizers can be contacted here

Program Committee


Jayadev Acharya
Dan Alistarh
Mohammad Alizadeh
Gustavo Alonso
David Andersen
Peter Bailis
Michael Carbin
Nicholas Carlini
Bryan Catanzaro
Tianqi Chen

Yu-Hsin Chen
Yuejie Chi
Minsik Cho
Christopher De Sa
Greg Diamos
Alex Dimakis
Lara Dolecek
Pradeep Dubey
Hadi Esmaeilzadeh
Kayvon Fatahalian

Phillip Gibbons
Garth Gibson
Anna Goldie
Justin Gottschlich
Kim Hazelwood
Cho-Jui Hsieh
Prateek Jain
Gauri Joshi
Rania Khalaf
Jakub Konečný

Farinaz Koushanfar
Sanmi Koyejo
Tim Kraska
Arun Kumar
Anastasios Kyrillidis
Aparna Lakshmiratan
Kangwook Lee
Hai (Helen) Li
Jing Li
Siyuan Ma

Diana Marculescu
Erik Meijer
Xiangrui Meng
Azalia Mirhoseini
Ioannis Mitliagkas
Rajat Monga
Andreas Moshovos
Jennifer Myers
Rob Nowak
Gennady Pekhimenko

Alex Ratner
Theodoros Rekatsinas
Bita Rouhani
Hanie Sedghi
Siddhartha Sen
Yakun Sophia Shao
Virginia Smith
Evan Sparks
Suvrit Sra

Martin Takac
Ameet Talwalkar
Joaquin Vanschoren
Shivaram Venkataraman
Marian Verhelst
Markus Weimer
Paul Whatmough
Andrew Wilson
Stephen Wright

Carole-Jean Wu
Neeraja Yadwadkar
Eiko Yoneki
Matei Zaharia
Ce Zhang

Important Dates

Conference Sessions Mon Mar 2nd through Wed the 4th
Workshop Application Opens Aug 02 02:59 PM PDT *
Paper Submission deadline Sep 09 05:00 PM PDT *
Author Feedback Begins Nov 11 04:59 PM PST *
Workshop Application Close Nov 15 05:00 PM PST *
Paper Rebuttal/discussion ends Nov 20 04:59 PM PST *
Workshop Notifications Nov 22 02:59 PM PST *
Sponsor Portal Open Dec 26 01:17 PM PST *
Paper Decision Notification Jan 03 02:59 PM PST *
Registration Opens Jan 28 02:00 PM PST *
Workshop Organizer Notifications Feb 07 03:59 PM PST *
Early pricing before this date. Feb 17 04:59 PM PST *
Camera Ready Deadline Feb 28 (Anywhere on Earth)
Last chance for a refund on registration fees Mar 01 04:00 PM PST *
All dates » * Dates above are in pacific time

About Machine Learning and Systems (MLSys)

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:

  • Computer architecture
  • Computer networks
  • Computer security
  • Databases
  • Design automation
  • Embedded & real-time systems
  • High-performance computing
  • Mobile computing
  • Measurement & perf. analysis
  • Operating systems
  • Programming languages
  • Software engineering

Read the whitepaper to learn more.