Poster Session

Accepted papers: https://www.amtl-workshop.org/accepted-papers

TuckER: Tensor Factorization for Knowledge Graph Completion Authors: Ivana Balazevic, Carl Allen, Timothy Hospedales

Learning Cancer Outcomes from Heterogeneous Genomic Data Sources: An Adversarial Multi-task Learning Approach Authors: Safoora Yousefi, Amirreza Shaban, Mohamed Amgad, Lee Cooper

Continual adaptation for efficient machine communication Authors: Robert Hawkins, Minae Kwon, Dorsa Sadigh, Noah Goodman

Every Sample a Task: Pushing the Limits of Heterogeneous Models with Personalized Regression Authors: Ben Lengerich, Bryon Aragam, Eric Xing

Data Enrichment: Multi-task Learning in High Dimension with Theoretical Guarantees Authors: Amir Asiaee, Samet Oymak, Kevin R. Coombes, Arindam Banerjee

A Functional Extension of Multi-Output Learning Authors: Alex Lambert, Romain Brault, Zoltan Szabo, Florence d'Alche-Buc

Interpretable Robust Recommender Systems with Side Information Authors: Wenyu Chen, Zhechao Huang, Jason Cheuk Nam Liang, Zihao Xu

Personalized Student Stress Prediction with Deep Multi-Task Network Authors: Abhinav Shaw, Natcha Simsiri, Iman Dezbani, Madelina Fiterau, Tauhidur Rahaman

SuperTML: Domain Transfer from Computer Vision to Structured Tabular Data through Two-Dimensional Word Embedding Authors: Baohua Sun, Lin Yang, Wenhan Zhang, Michael Lin, Patrick Dong, Charles Young, Jason Dong

Goal-conditioned Imitation Learning Authors: Yiming Ding, Carlos Florensa, Mariano Phielipp, Pieter Abbeel

Tasks Without Borders: A New Approach to Online Multi-Task Learning Authors: Alexander Zimin, Christoph H. Lampert

The Role of Embedding-complexity in Domain-invariant Representations Authors: Ching-Yao Chuang, Antonio Torralba, Stefanie Jegelka

Lifelong Learning via Online Leverage Score Sampling Authors: Dan Teng, Sakyasingha Dasgupta

Connections Between Optimization in Machine Learning and Adaptive Control Authors: Joseph E. Gaudio, Travis E. Gibson, Anuradha M. Annaswamy, Michael A. Bolender, Eugene Lavretsky

Meta-Reinforcement Learning for Adaptive Autonomous Driving Authors: Yesmina Jaafra, Jean Luc Laurent, Aline Deruyver, Mohamed Saber Naceur

PAGANDA: An Adaptive Task-Independent Automatic Data Augmentation Authors: Boli Fang, Miao Jiang, Jerry Shen

Improving Relevance Prediction with Transfer Learning in Large-scale Retrieval Systems Authors: Ruoxi Wang, Zhe Zhao, Xinyang Yi, Ji Yang, Derek Zhiyuan Cheng, Lichan Hong, Steve Tjoa, Jieqi Kang, Evan Ettinger, Ed Chi

Federated Optimization for Heterogeneous Networks Authors: Tian Li*, Anit Kumar Sahu*, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, Virginia Smith

Learning Exploration Policies for Model-Agnostic Meta-Reinforcement Learning Authors: Swaminathan Gurumurthy, Sumit Kumar, Katia Sycara

A Meta Understanding of Meta-Learning Authors: Wei-Lun Chao, Han-Jia Ye, De-Chuan Zhan, Mark Campbell, Kilian Q. Weinberger

Multi-Task Learning via Task Multi-Clustering Authors: Andy Yan, Xin Wang, Ion Stoica, Joseph Gonzalez, Roy Fox

Prototypical Bregman Networks Authors: Kubra Cilingir, Brian Kulis

Differentiable Hebbian Plasticity for Continual Learning Authors: Vithursan Thangarasa, Thomas Miconi, Graham W. Taylor

Active Multitask Learning with Committees Authors: Jingxi Xu, Da Tang, Tony Jebara

Progressive Memory Banks for Incremental Domain Adaptation Authors: Nabiha Asghar, Lili Mou, Kira A. Selby, Kevin D. Pantasdo, Pascal Poupart, Xin Jiang

Sub-policy Adaptation for Hierarchical Reinforcement Learning Authors: Alexander Li, Carlos Florensa, Pieter Abbeel

Learning to learn to communicate Authors: Ryan Lowe*, Abhinav Gupta*, Jakob Foerster, Douwe Kiela, Joelle Pineau