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LL@ICML 2017 : Workshop on Lifelong Learning: A Reinforcement Learning Approach @ICML 2017


When Aug 10, 2017 - Aug 10, 2017
Where Sydney, Australia
Submission Deadline Jun 6, 2017
Notification Due Jun 23, 2017
Categories    machine learning   reinforcement learning   deep learning   artificial intelligence

Call For Papers

Dear Colleagues,

We would like to invite you to submit extended abstracts of between 4-6 pages to our 'Lifelong Learning: A Reinforcement Learning Approach' ICML 2017 Workshop which will be held in Sydney, Australia on August 10, 2017.

Date: 10 August 2017
Location: Sydney, Australia
Submission deadline: 6th June 2017, 11:59 PM (GMT+2)

One of the most challenging and open problems in Artificial Intelligence (AI) is that of Lifelong Learning:

“Lifelong Learning is the continued learning of tasks, from one or more domains, over the course of a lifetime, by a lifelong learning system. A lifelong learning system efficiently and effectively (1) retains the knowledge it has learned; (2) selectively transfers knowledge to learn new tasks; and (3) ensures the effective and efficient interaction between (1) and (2).”

Lifelong learning is still in its infancy. Many issues currently exist such as learning general representations, catastrophic forgetting , efficient knowledge retention mechanisms and hierarchical abstractions . Much work has been done in the Reinforcement Learning (RL) community to tackle different elements of lifelong learning. Active research topics include hierarchical abstractions, transfer learning, multi-task learning and curriculum learning. With the emergence of powerful function approximators such as in Deep Learning, we feel that now is a perfect time to provide a forum to discuss ways to move forward and provide a truly general lifelong learning framework, using RL-based algorithms, with more rigour than ever before. This workshop will endeavour to promote interaction between researchers working on the different elements of lifelong learning to try and find a synergy between the various techniques.

The submitted work should be an extended abstract of between 4-6 pages (including references). The submission should be in pdf format and should follow the style guidelines for ICML 2017. The review process is double-blind and the work should be submitted by the latest 6th June 2017, 11:59 PM(GMT+2). Submissions must be made through easychair:

Using Hierarchical Abstractions to perform lifelong learning (e.g., skills/options and state space representations)
Transfer Learning
Multi-task Learning
Curriculum Learning
Deep Learning as a tool for performing lifelong learning
Determine new, challenging benchmark domains

For more info see our website.

Sarath Chandar - University of Montreal
Balaraman Ravindran - Indian Institute of Technology
Daniel J. Mankowitz - Technion Israel Institute of Technology
Tom Zahavy - Technion Israel Institute of Technology
Shie Mannor - Technion Israel Institute of Technology

We look forward to reviewing your submissions and hope to see you in Sydney!

Kind regards,
Sarath, Ravi, Daniel, Tom and Shie

Lifelong Learning: A Reinforcement Learning Approach Workshop organizers

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