Timezone: »

 
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 · Alexander Li · Ryan Lowe

Sat Jun 15 11:00 AM -- 12:00 PM (PDT) @ None

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

Author Information

Ivana Balazevic (University of Edinburgh)
Minae Kwon (Stanford University)
Benjamin Lengerich (Carnegie Mellon University)
Amir Asiaee (The Ohio State University)
Alex Lambert (Télécom ParisTech)
Wenyu Chen (MIT)
Yiming Ding (University of California, Berkeley)
Carlos Florensa (UC Berkeley)
Joseph E Gaudio (MIT)
Yesmina Jaafra (Segula Technologies - Icube Laboratory)
Boli Fang (Indiana University)
Ruoxi Wang (Google AI)
Tian Li (CMU)
SWAMINATHAN GURUMURTHY (Carnegie Mellon University)
Andy Yan (University of California, Berkeley)
Kubra Cilingir (Boston University / Amazon)
Vithursan (Vithu) Thangarasa (University of Guelph/ Vector Institute)

I'm currently a Graduate Student Research at the University of Guelph and Vector Institute for Artificial Intelligence working on continual learning algorithms and overcoming catastrophic forgetting for deep neural networks. As a graduate student advised by Dr. Graham W. Taylor, I am working towards an MASc at University of Guelph's Machine Learning Research Group (MLRG) and completed my BEng. in Engineering Systems and Computing, along with six awesome internships at Uber AI, Tesla, Scotiabank, ON Semiconductor, Evertz Microsystems, and Jamdeo.

Alexander Li (UC Berkeley)

Interested in representation learning, unsupervised learning, meta-learning, and hierarchical reinforcement learning.

Ryan Lowe (Mila, McGill University)

More from the Same Authors