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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 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

08:30 AM Opening Remarks
08:40 AM Building and Structuring Training Sets for Multi-Task Learning (Alex Ratner) (Invited Talk)|| Video »  Alexander J Ratner
09:10 AM Meta-Learning: Challenges and Frontiers (Chelsea Finn) (Invited Talk)|| Video »  Chelsea Finn
09:40 AM Contributed Talk: Learning Exploration Policies for Model-Agnostic Meta-Reinforcement Learning (Contributed Talk)|| Video » 
09:55 AM Contributed Talk: Lifelong Learning via Online Leverage Score Sampling (Contributed Talk)|| Video » 
10:10 AM Tricks of the Trade 1 (Rich Caruana) (Lightning Talk)|| Rich Caruana
10:25 AM Coffee Break
11:00 AM Poster Session
Ivana Balazevic, Minae Kwon, Benjamin Lengerich, Amir Asiaee, Alex Lambert, Wenyu Chen, Yiming Ding, Carlos Florensa, Joseph E Gaudio, Yesmina Jaafra, Boli Fang, Ruoxi Wang, Tian Li, SWAMINATHAN GURUMURTHY, Andy Yan, Kubra Cilingir, Vithursan (Vithu) Thangarasa, Alex Li, Ryan Lowe
12:00 PM Lunch Break
01:45 PM ARUBA: Efficient and Adaptive Meta-Learning with Provable Guarantees (Ameet Talwalkar) (Invited Talk)|| Video »  Ameet Talwalkar
02:15 PM Efficient Lifelong Learning Algorithms: Regret Bounds and Statistical Guarantees (Massimiliano Pontil) (Invited Talk)|| Video » 
02:45 PM Tricks of Trade 2 (Rich Caruana) (Lightning Talk)|| Video »  Rich Caruana
03:00 PM Coffee Break
03:30 PM Multi-Task Learning in the Wilderness (Andrej Karpathy) (Invited Talk)|| Video »  Andrej Karpathy
04:00 PM Recent Trends in Personalization: A Netflix Perspective (Justin Basilico) (Invited Talk)|| Video »  Justin Basilico
04:30 PM Contributed Talk: Improving Relevance Prediction with Transfer Learning in Large-scale Retrieval Systems (Contributed Talk)|| Video »  Ruoxi Wang
04:45 PM Contributed Talk: Continual Adaptation for Efficient Machine Communication (Contributed Talk)|| Video »  Minae Kwon
05:00 PM Toward Robust AI Systems for Understanding and Reasoning Over Multimodal Data (Hannaneh Hajishirzi) (Invited Talk)|| Video » 
05:30 PM Closing Remarks

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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