ICML 2013

ICML 2013
Name	International Conference on Machine Learning 2013
Abbreviation	ICML 2013
Location	Seattle, Washington
Venue	Washington State Convention Center
Dates	2013
Discipline	Machine learning
Organizer	International Machine Learning Society

ICML 2013 ICML 2013 was the 30th annual meeting of the International Machine Learning Society bringing together researchers from Microsoft Research, Google Research, IBM Research, Facebook AI Research, Amazon Lab126, Apple Inc., Yahoo! Research, NVIDIA Research, and leading universities. The conference in Seattle, Washington showcased advances from laboratories such as Stanford University, Massachusetts Institute of Technology, University of California, Berkeley, Carnegie Mellon University, and University of Toronto, with participation by scholars from University of Oxford, University of Cambridge, ETH Zurich, Imperial College London, and University of Montreal.

Overview

ICML 2013 featured program chairs and organizers from Stanford University, University of Edinburgh, Princeton University, University of Washington, and Columbia University. The meeting included plenaries, parallel sessions, poster sessions, workshops, tutorials, and a program committee drawn from Harvard University, Yale University, Cornell University, University of Illinois Urbana-Champaign, and University of Michigan. Attendees included representatives from Intel Labs, Qualcomm Research, Siemens Corporate Research, Bell Labs, and Adobe Research, fostering collaborations with labs at Google X, DeepMind, OpenAI, Baidu Research, and Tencent AI Lab.

Conference Organization

The organizational structure involved program chairs affiliated with University College London and Purdue University, local arrangements by University of Washington, sponsorship from NSF, DARPA, EPSRC, and corporate sponsors Google, Microsoft, IBM, and Facebook. The program committee comprised experts from NYU, Duke University, Rice University, University of California, San Diego, University of British Columbia, McGill University, Weizmann Institute of Science, Tel Aviv University, Seoul National University, and Tsinghua University. The steering committee included members associated with NeurIPS, COLT, AAAI, IJCAI, and KDD.

Program and Keynotes

Keynote speakers represented institutions such as Stanford University, MIT, Carnegie Mellon University, University of Toronto, and Princeton University. Invited talks referenced research lines from Google DeepMind, Microsoft Research Redmond, IBM Watson, Facebook AI Research New York, and Amazon AI. Session chairs originated from Caltech, Brown University, Australian National University, University of Melbourne, University of Sydney, and University of Adelaide. Panel discussions involved figures affiliated with Royal Society, IEEE, ACM, Royal Society of Canada, and Max Planck Institute for Intelligent Systems.

Accepted Papers and Highlights

Accepted papers covered topics spanning contributions from groups at Stanford University, MIT CSAIL, UC Berkeley AI Research, Carnegie Mellon University Machine Learning Department, University of Toronto Vector Institute, Google Brain, Microsoft Research Cambridge, IBM T.J. Watson Research Center, Yahoo! Labs, Amazon AI, NVIDIA, DeepMind Technologies, OpenAI Research, and Baidu Research USA. Highlights included works on probabilistic graphical models from University of Washington, sparse methods from ETH Zurich, kernel methods from Imperial College London, optimization algorithms from Princeton University, online learning from Columbia University, bandit algorithms from University College London, and deep learning architectures from NYU Courant Institute. Notable methodological advances connected to research at Weizmann Institute of Science, Tel Aviv University, Seoul National University, Peking University, Tsinghua University, and Shanghai Jiao Tong University.

Workshops and Tutorials

Workshops and tutorials were organized by teams from Stanford HAI, MIT-IBM Watson AI Lab, Berkeley AI Research, CMU Robotics Institute, Oxford Machine Learning Research Group, Cambridge Machine Learning Group, ETH Machine Learning Group, and Imperial College AI Group. Topics included collaborations with Google Research Brain Team, Microsoft Research AI, Facebook AI Research Paris, Amazon Alexa AI, Apple Machine Learning Research, and Yahoo! Research Barcelona. Specialized workshops involved partnerships with ICLR community, NeurIPS community, COLT community, AAAI community, and SIGKDD.

Awards and Recognition

Paper awards and recognitions were presented with involvement from ACM SIGKDD, IEEE Computational Intelligence Society, Royal Society Fellows, Turing Award laureates in attendance, and representatives from National Academy of Sciences, American Philosophical Society, Royal Society of Edinburgh, and Canadian Institute for Advanced Research. Young researcher awards and best paper recognitions highlighted contributions from scholars at Harvard University, Yale University, Princeton University, University of Toronto, and McGill University.

Impact and Legacy

The outcomes influenced subsequent meetings at NeurIPS 2014, ICLR 2014, AAAI 2014, KDD 2014, and COLT 2014, and informed research agendas at Google DeepMind, OpenAI, DeepLearning.ai, Vector Institute, CIFAR, and Montreal Institute for Learning Algorithms. Technologies and collaborations seeded by presentations at the conference propagated to industrial projects at Microsoft Azure, Google Cloud Platform, Amazon Web Services, IBM Cloud, Facebook Infrastructure, NVIDIA DRIVE, and Tesla Autopilot. The conference strengthened links among universities, research labs, funding agencies, professional societies, and industry consortia.

Category:Machine learning conferences