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Adaptive and Multitask Learning: Algorithms & Systems
Maruan Al-Shedivat · Anthony Platanios · Otilia Stretcu · Jacob Andreas · Ameet Talwalkar · Rich Caruana · Tom Mitchell · Eric Xing

Sat Jun 15 08:30 AM -- 06:00 PM (PDT) @ Seaside Ballroom
Event URL: https://www.amtl-workshop.org/ »

Driven by progress in deep learning, the machine learning community is now able to tackle increasingly more complex problems—ranging from multi-modal reasoning to dexterous robotic manipulation—all of which typically involve solving nontrivial combinations of tasks. Thus, designing adaptive models and algorithms that can efficiently learn, master, and combine multiple tasks is the next frontier. AMTL workshop aims to bring together machine learning researchers from areas ranging from theory to applications and systems, to explore and discuss:

* advantages, disadvantages, and applicability of different approaches to learning in multitask settings,
* formal or intuitive connections between methods developed for different problems that help better understand the landscape of multitask learning techniques and inspire technique transfer between research lines,
* fundamental challenges and open questions that the community needs to tackle for the field to move forward.

Webpage: www.amtl-workshop.org

Author Information

Maruan Al-Shedivat (Carnegie Mellon University)
Anthony Platanios (Carnegie Mellon University)
Otilia Stretcu (Carnegie Mellon University)
Jacob Andreas (UC Berkeley)
Ameet Talwalkar (Carnegie Mellon University)
Rich Caruana (Microsoft)
Tom Mitchell (Carnegie Mellon University)
Tom Mitchell

Tom M. Mitchell is the Founders University Professor and Interim Dean of the School of Computer Science at Carnegie Mellon University. Mitchell has worked in Machine Learning for many years, and co-founded the ICML conference (with Jaime Carbonell and Ryszard Michalski). Recently, he directed the Never-Ending Language Learning (NELL) project, which operated continuously for over eight years, providing a case study for how to architect never-ending learning systems. Mitchell is a member of the U.S. National Academy of Engineering, a member of the American Academy of Arts and Sciences, and a Fellow and Past President of the Association for the Advancement of Artificial Intelligence (AAAI).

Eric Xing (Petuum Inc. and CMU)

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