1st Conference on Lifelong Learning Agents (CoLLAs) - Registration open

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Razvan Pascanu

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Jun 9, 2022, 5:45:00 AM6/9/22
to ml-...@googlegroups.com, Doina Precup, sarath....@mila.quebec

Dear All,


Apologies for cross-posting.


Registration (https://lifelong-ml.cc/registration) for 1st Conference on Lifelong Learning Agents (CoLLAs) 2022 is open. This will be a hybrid event held in Montreal, Canada (more information about the venue https://lifelong-ml.cc/venue). The conference will have a purely virtual 2 days 18th-19th of August, with 22nd-24th of August being both in-person and virtual. 


We have already a list of accepted speakers for the conference which includes Abhinav Gupta (Carnegie Mellon University), Claudia Clopath (Imperial College London), Hanie Sedghi (Google Brain), Hugo Larochelle (Google Brain & Mila), Rich Caruna (Microsoft Research),  Tinne Tuytelaars (Katholieke Universiteit, Leuven)  and Yoshua Bengio (University of Montreal & Mila). See https://lifelong-ml.cc/speakers.


Please follow us on twitter (https://twitter.com/CoLLAs_Conf) and check our website for more updates and details about the conference (https://lifelong-ml.cc). 


Motivation for the conference:


Machine learning has relied heavily on a traditional view of the learning process, whereby observations are assumed to be i.i.d., typically given as a dataset split into a training and validation set with the explicit focus to maximize performance on the latter. While this view proved to be immensely beneficial for the field, it represents just a fraction of the realistic scenarios of interest. Over the past few decades, increased attention has been given to alternative paradigms that help explore different aspects of the learning process, from Lifelong Learning, Continual Learning, and Meta-Learning to Transfer Learning, Multi-Task Learning and Out-Of-Distribution Generalization to name just a few.


The Conference on Lifelong Learning Agents (CoLLAs) will focus on these learning paradigms that aim to move beyond the traditional, single-distribution machine learning setting and to allow learning to be more robust, more efficient in terms of compute and data, more versatile in terms of being able to handle multiple problems and be well-defined and well-behaved in more realistic non-stationary settings compared to the traditional view. 


For any questions about the conference, you can contact us at con...@lifelong-ml.cc.

 

Regards,

Doina Precup, Sarath Chandar, Razvan Pascanu

CoLLAs 2022 General and Program Chairs


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