Peer Reviewed Publications

2020

Learning Hierarchical Teaching Policies for Cooperative Agents.
Dong-Ki Kim, Miao Liu, Shayegan Omidshafiei, Sebastian Lopez-Cot, Matthew Riemer, Golnaz Habibi, Gerald Tesauro, Sami Mourad, Murray Campbell, and Jonathan P. How.
Proceedings of the 19th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2020).
[Paper]

 

On the Role of Weight Sharing During Deep Option Learning.
Matthew Riemer, Ignacio Cases, Clemens Rosenbaum, Miao Liu, and Gerald Tesauro.
Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI 2020).
[Paper]

 

 

2019

 

Recursive Routing Networks: Learning to Compose Modules for Language Understanding.
Ignacio Cases, Clemens Rosenbaum, Matthew Riemer, Atticus Geiger, Tim Klinger, Alex Tamkin, Olivia Li, Sandhini Agarwal, Joshua Greene, Dan Jurafsky, Christopher Potts, and Lauri Karttunen.
Proceedings of 17th Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2019).
[Paper][Data]

 

 

 

Learning to Learn without Forgetting by Maximizing Transfer and Minimizing Interference.
Matthew Riemer, Ignacio Cases, Robert Ajemian, Miao Liu, Irina Rish, Yuhai Tu, and Gerald Tesauro.
Proceedings of Seventh International Conference on Learning Representations (ICLR 2019).
[Paper][Website][Code][Blog Post]

 

 

Heterogeneous Knowledge Transfer via Hierarchical Teaching in Cooperative Multiagent Reinforcement Learning.
Dong-Ki Kim, Miao Liu, Shayegan Omidshafiei, Sebastian Lopez-Cot, Matthew Riemer, Gerald Tesauro, Murray Campbell, Sami Mourad, Golnaz Habibi, and Jonathan P. How.
In AAAI 2019 Workshop on Reinforcement Learning in Games.
[Paper]

 

 

 

Scalable Recollections for Continual Lifelong Learning.
Matthew Riemer, Tim Klinger, Djallel Bouneffouf, and Michele Franceschini.
Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence (AAAI 2019).
[Paper]

 

 

 

Learning to Teach in Cooperative Multiagent Reinforcement Learning.
Shayegan Omidshafiei, Dong-Ki Kim, Miao Liu, Gerald Tesauro, Matthew Riemer, Christopher Amato, Murray Campbell, and Jonathan P How.
Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence (AAAI 2019).
Outstanding Student Paper Honorable Mention.
[Paper]

2018

 

Continual Learning by Maximizing Transfer and Minimizing Interference.
Matthew Riemer, Ignacio Cases, Robert Ajemian, Miao Liu, Irina Rish, Yuhai Tu, and Gerald Tesauro.
In NeurIPS 2018 Workshop on Continual Learning.
[Paper][Spotlight Slides]

 

 

 

Continual Learning with Self-Organizing Maps.
Pouya Bashivan, Martin Schrimpf, Robert Ajemian, Irina Rish, Matthew Riemer and Yuhai Tu.
In NeurIPS 2018 Workshop on Continual Learning.
[Paper]

 

PepCVAE: Semi-Supervised Targeted Design of Antimicrobial Peptide Molecules.
Payel Das, Kahini Wadhawan, Oscar Chang, Tom Sercu, Cicero dos Santos, Matthew Riemer, Vijil Chenthamarakshan, Inkit Padhi, and Aleksandra Mojsilovic.
In NeurIPS 2018 Workshop on Machine Learning for Molecules and Materials.
[Paper]

 

Learning Abstract Options.
Matthew Riemer, Miao Liu, and Gerald Tesauro.
Proceedings of the 32nd Conference on Neural Information Processing Systems (NeurIPS 2018).
[Paper]

 

Learning to Teach in Cooperative Multiagent Reinforcement Learning.
Shayegan Omidshafiei, Dong-Ki Kim, Miao Liu, Gerald Tesauro, Matthew Riemer, Christopher Amato, Murray Campbell, and Jonathan P How.
In ICML 2018 Workshop on Lifelong Learning.
[Paper]

 

Routing Networks: Adaptive Selection of Non-linear Functions for Multi-Task Learning.
Clemens Rosenbaum, Tim Klinger, and Matthew Riemer.
Proceedings of Sixth International Conference on Learning Representations (ICLR 2018).
[Paper][Code]

2017

 

Generative Knowledge Distillation for General Purpose Function Compression.
Matthew Riemer, Michele Franceschini, Djallel Bouneffouf, and Tim Klinger.
In NeurIPS 2017 Workshop on Teaching Machines, Robots, and Humans.
[Paper]

 

 

Routing Networks: Adaptive Selection of Non-linear Functions for Multi-Task Learning.
Clemens Rosenbaum, Tim Klinger, and Matthew Riemer.
In NeurIPS 2017 Workshop on Meta-Learning.
[Paper]

Earlier

 

 

Correcting Forecasts with Multifactor Neural Attention.
Matthew Riemer, Aditya Vempaty, Flavio P. Calmon, Fenno F. Heath III, Richard Hull, and Elham Khabiri.
Proceedings of the 33rd International Conference on Machine Learning (ICML 2016).
[Paper][Slides]

 

 

 

Domain Scoping for Subject Matter Experts.
Elham Khabiri, Matthew Riemer, Fenno F. Heath III, and Richard Hull.
Association for the Advancement of Artificial Intelligence (AAAI) 2015 Workshop on Cognitive Assistance in Government.
[Paper]

 

 

 

 

Alexandria: Extensible Framework for Rapid Exploration of Social Media.
Fenno Heath III, Richard Hull, Elham Khabiri, Matthew Riemer, Noi Sukaviriya, and Roman Vaculin.
In IEEE BigData Congress 2015.
[Paper]

 

 

 

 

A Deep Learning and Knowledge Transfer Based Architecture for Social Media User Characteristic Determination.
Matthew Riemer, Sophia Krasikov, and Harini Srinivasan.
In NAACL-HLT 2015 Workshop on Natural Language Processing for Social Media (SocialNLP).
[Paper][Slides]

 

 

 

 

Bias Voltage Dependence of the Total Magnetic Field in CoFeB Magnetic Tunnel Junctions.
Matthew Riemer, Jonathan Sun, and Andrew Kent.
March 2011 Meeting of the American Physical Society.
[Slides]

 

 

 

Public Patent Disclosures

Facilitating mapping of control policies to regulatory documents. [Link]

Time series forecasting to determine relative causal impact. [Link]

 

Textbook Chapters

Distributed Computing in Social Media Analytics.
Matthew Riemer.
Chapter in Distributed Computing in Big Data Analytics, Springer 2017.
[Link]