COLT: Computational Learning Theory

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Past:   Proceedings on DBLP

Future:  Post a CFP for 2022 or later   |   Invite the Organizers Email

 
 

All CFPs on WikiCFP

Event When Where Deadline
COLT 2021 Computational Learning Theory
Aug 15, 2021 - Aug 19, 2021 Boulder, Colorado Jan 29, 2021
COLT 2020 Conference on Learning Theory (COLT)
Jul 9, 2020 - Jul 12, 2020 Graz, Austria Jan 31, 2020
COLT 2019 Computational Learning Theory
Jun 25, 2019 - Jun 29, 2019 Phoenix, AZ, USA Feb 1, 2019
COLT 2018 Computational Learning Theory
Jul 5, 2018 - Jul 9, 2018 Stockholm, Sweden Feb 16, 2018
COLT 2017 Computational Learning Theory
Jul 7, 2017 - Jul 10, 2017 Amsterdam, Netherlands Feb 17, 2017
COLT 2016 Conference on Learning Theory
Jun 23, 2016 - Jun 26, 2016 New York, NY, USA Feb 12, 2016
COLT 2015 Conference on Learning Theory 2015
Jul 3, 2015 - Jul 3, 2015 Paris, France Feb 19, 2015
COLT 2014 Conference on Learning Theory
Jun 13, 2014 - Jun 15, 2014 Barcelona, Spain Feb 7, 2014
COLT 2013 26th Annual Conference on Learning Theory
Jun 12, 2013 - Jun 14, 2013 Princeton, NJ, USA Feb 8, 2013
COLT 2012 25th Annual Conference on Learning Theory
Jun 25, 2012 - Jun 27, 2012 Edinburgh, Scotland Feb 14, 2012
COLT 2011 The 24rd Annual Conference on Learning Theory
Jul 9, 2011 - Jul 11, 2011 Budapest, Hungary Feb 11, 2011
COLT 2010 The 23rd International Conference on Learning Theory
Jun 27, 2010 - Jun 29, 2010 Haifa, Israel Feb 19, 2010
COLT 2009 The 22nd Annual Conference on Learning Theory
Jun 18, 2009 - Jun 21, 2009 Montreal, Canada Feb 13, 2009
COLT 2008 Conference on Learning Theory
Jul 9, 2008 - Jul 12, 2008 Helsinki, Finland Feb 20, 2008
 
 

Present CFP : 2021

The 34th Annual Conference on Learning Theory (COLT 2021) will take place August 15-19, 2021. Assuming the circumstances allow for an in-person conference it will be held at CU Boulder, in Colorado. We invite submissions of papers addressing theoretical aspects of machine learning, broadly defined as a subject at the intersection of computer science, statistics and applied mathematics. We strongly support an inclusive view of learning theory, including fundamental theoretical aspects of learnability in various contexts, and theory that sheds light on empirical phenomena.
 

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