Matthew Riemer

According to our database, Matthew Riemer authored at least 42 papers between 2015 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Can Large Language Models Adapt to Other Agents In-Context?

Matthew Riemer, Zahra Ashktorab, Djallel Bouneffouf, Payel Das, Miao Liu, Justin D. Weisz, Murray Campbell

CoRR, 2024

Enabling Realtime Reinforcement Learning at Scale with Staggered Asynchronous Inference.

Matthew Riemer, Gopeshh Subbaraj, Glen Berseth, Irina Rish

CoRR, 2024

Combining Domain and Alignment Vectors to Achieve Better Knowledge-Safety Trade-offs in LLMs.

Megh Thakkar, Yash More, Quentin Fournier, Matthew Riemer, Pin-Yu Chen, Amal Zouaq, Payel Das, Sarath Chandar

CoRR, 2024

Contextual Moral Value Alignment Through Context-Based Aggregation.

Pierre L. Dognin, Jesus Rios, Ronny Luss, Inkit Padhi, Matthew D. Riemer, Miao Liu, Prasanna Sattigeri, Manish Nagireddy, Kush R. Varshney, Djallel Bouneffouf

CoRR, 2024

ComVas: Contextual Moral Values Alignment System.

Inkit Padhi, Pierre L. Dognin, Jesus Rios, Ronny Luss, Swapnaja Achintalwar, Matthew Riemer, Miao Liu, Prasanna Sattigeri, Manish Nagireddy, Kush R. Varshney, Djallel Bouneffouf

Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, 2024

A Deep Dive into the Trade-Offs of Parameter-Efficient Preference Alignment Techniques.

Megh Thakkar, Quentin Fournier, Matthew Riemer, Pin-Yu Chen, Amal Zouaq, Payel Das, Sarath Chandar

Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2024

2023

Learning in Factored Domains with Information-Constrained Visual Representations.

Tyler Malloy, Miao Liu, Matthew D. Riemer, Tim Klinger, Gerald Tesauro, Chris R. Sims

CoRR, 2023

2022

Towards Continual Reinforcement Learning: A Review and Perspectives.

Khimya Khetarpal, Matthew Riemer, Irina Rish, Doina Precup

J. Artif. Intell. Res., 2022

Game-Theoretical Perspectives on Active Equilibria: A Preferred Solution Concept over Nash Equilibria.

Dong-Ki Kim, Matthew Riemer, Miao Liu, Jakob N. Foerster, Gerald Tesauro, Jonathan P. How

CoRR, 2022

Continual Learning In Environments With Polynomial Mixing Times.

Matthew Riemer, Sharath Chandra Raparthy, Ignacio Cases, Gopeshh Subbaraj, Maximilian Puelma Touzel, Irina Rish

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Influencing Long-Term Behavior in Multiagent Reinforcement Learning.

Dong-Ki Kim, Matthew Riemer, Miao Liu, Jakob N. Foerster, Michael Everett, Chuangchuang Sun, Gerald Tesauro, Jonathan P. How

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Context-Specific Representation Abstraction for Deep Option Learning.

Marwa Abdulhai, Dong-Ki Kim, Matthew Riemer, Miao Liu, Gerald Tesauro, Jonathan P. How

Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021

Sequoia: A Software Framework to Unify Continual Learning Research.

Fabrice Normandin, Florian Golemo, Oleksiy Ostapenko, Pau Rodríguez, Matthew D. Riemer, Julio Hurtado, Khimya Khetarpal, Dominic Zhao, Ryan Lindeborg, Timothée Lesort, Laurent Charlin, Irina Rish, Massimo Caccia

CoRR, 2021

Efficient Black-Box Planning Using Macro-Actions with Focused Effects.

Cameron Allen, Michael Katz, Tim Klinger, George Konidaris, Matthew Riemer, Gerald Tesauro

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2021

A Policy Gradient Algorithm for Learning to Learn in Multiagent Reinforcement Learning.

Dong-Ki Kim, Miao Liu, Matthew Riemer, Chuangchuang Sun, Marwa Abdulhai, Golnaz Habibi, Sebastian Lopez-Cot, Gerald Tesauro, Jonathan P. How

Proceedings of the 38th International Conference on Machine Learning, 2021

Modeling Capacity-Limited Decision Making Using a Variational Autoencoder.

Tyler Malloy, Tim Klinger, Miao Liu, Gerald Tesauro, Matthew Riemer, Chris R. Sims

Proceedings of the 43rd Annual Meeting of the Cognitive Science Society, 2021

Capacity-Limited Decentralized Actor-Critic for Multi-Agent Games.

Tyler Malloy, Chris R. Sims, Tim Klinger, Miao Liu, Matthew Riemer, Gerald Tesauro

Proceedings of the 2021 IEEE Conference on Games (CoG), 2021

RL Generalization in a Theory of Mind Game Through a Sleep Metaphor (Student Abstract).

Tyler Malloy, Tim Klinger, Miao Liu, Gerald Tesauro, Matthew Riemer, Chris R. Sims

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020

Consolidation via Policy Information Regularization in Deep RL for Multi-Agent Games.

Tyler Malloy, Tim Klinger, Miao Liu, Matthew Riemer, Gerald Tesauro, Chris R. Sims

CoRR, 2020

Deep RL With Information Constrained Policies: Generalization in Continuous Control.

Tyler Malloy, Chris R. Sims, Tim Klinger, Miao Liu, Matthew Riemer, Gerald Tesauro

CoRR, 2020

A Study of Compositional Generalization in Neural Models.

Tim Klinger, Dhaval Adjodah, Vincent Marois, Josh Joseph, Matthew Riemer, Alex 'Sandy' Pentland, Murray Campbell

CoRR, 2020

Finding Macro-Actions with Disentangled Effects for Efficient Planning with the Goal-Count Heuristic.

Cameron Allen, Tim Klinger, George Konidaris, Matthew Riemer, Gerald Tesauro

CoRR, 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, Jonathan P. How

Proceedings of the 19th International Conference on Autonomous Agents and Multiagent Systems, 2020

On the Role of Weight Sharing During Deep Option Learning.

Matthew Riemer, Ignacio Cases, Clemens Rosenbaum, Miao Liu, Gerald Tesauro

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

Hierarchical Average Reward Policy Gradient Algorithms (Student Abstract).

Akshay Dharmavaram, Matthew Riemer, Shalabh Bhatnagar

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019

Hierarchical Average Reward Policy Gradient Algorithms.

Akshay Dharmavaram, Matthew Riemer, Shalabh Bhatnagar

CoRR, 2019

Routing Networks and the Challenges of Modular and Compositional Computation.

Clemens Rosenbaum, Ignacio Cases, Matthew Riemer, Tim Klinger

CoRR, 2019

Continual Learning with Self-Organizing Maps.

Pouya Bashivan, Martin Schrimpf, Robert Ajemian, Irina Rish, Matthew Riemer, Yuhai Tu

CoRR, 2019

Learning Hierarchical Teaching in Cooperative Multiagent Reinforcement Learning.

Dong-Ki Kim, Miao Liu, Shayegan Omidshafiei, Sebastian Lopez-Cot, Matthew Riemer, Golnaz Habibi, Gerald Tesauro, Sami Mourad, Murray Campbell, Jonathan P. How

CoRR, 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 D. Greene, Dan Jurafsky, Christopher Potts, Lauri Karttunen

Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2019

Learning to Learn without Forgetting by Maximizing Transfer and Minimizing Interference.

Matthew Riemer, Ignacio Cases, Robert Ajemian, Miao Liu, Irina Rish, Yuhai Tu, Gerald Tesauro

Proceedings of the 7th International Conference on Learning Representations, 2019

Scalable Recollections for Continual Lifelong Learning.

Matthew Riemer, Tim Klinger, Djallel Bouneffouf, Michele Franceschini

Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

Learning to Teach in Cooperative Multiagent Reinforcement Learning.

Shayegan Omidshafiei, Dong-Ki Kim, Miao Liu, Gerald Tesauro, Matthew Riemer, Christopher Amato, Murray Campbell, Jonathan P. How

Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

2018

PepCVAE: Semi-Supervised Targeted Design of Antimicrobial Peptide Sequences.

Payel Das, Kahini Wadhawan, Oscar Chang, Tom Sercu, Cícero Nogueira dos Santos, Matthew Riemer, Inkit Padhi, Vijil Chenthamarakshan, Aleksandra Mojsilovic

CoRR, 2018

Learning Abstract Options.

Matthew Riemer, Miao Liu, Gerald Tesauro

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Routing Networks: Adaptive Selection of Non-Linear Functions for Multi-Task Learning.

Clemens Rosenbaum, Tim Klinger, Matthew Riemer

Proceedings of the 6th International Conference on Learning Representations, 2018

2017

Generation and Consolidation of Recollections for Efficient Deep Lifelong Learning.

Matthew Riemer, Michele Franceschini, Tim Klinger

CoRR, 2017

Representation Stability as a Regularizer for Improved Text Analytics Transfer Learning.

Matthew Riemer, Elham Khabiri, Richard Goodwin

CoRR, 2017

2016

Correcting Forecasts with Multifactor Neural Attention.

Matthew Riemer, Aditya Vempaty, Flávio P. Calmon, Fenno F. Terry Heath III, Richard Hull, Elham Khabiri

Proceedings of the 33nd International Conference on Machine Learning, 2016

2015

Alexandria: Extensible Framework for Rapid Exploration of Social Media.

Fenno F. Terry Heath III, Richard Hull, Elham Khabiri, Matthew Riemer, Noi Sukaviriya, Roman Vaculín

Proceedings of the 2015 IEEE International Congress on Big Data, New York City, NY, USA, June 27, 2015

A Deep Learning and Knowledge Transfer Based Architecture for Social Media User Characteristic Determination.

Matthew Riemer, Sophia Krasikov, Harini Srinivasan

Proceedings of the third International Workshop on Natural Language Processing for Social Media, 2015

Domain Scoping for Subject Matter Experts.

Elham Khabiri, Matthew Riemer, Fenno F. Terry Heath III, Richard Hull

Proceedings of the 2015 AAAI Fall Symposia, Arlington, Virginia, USA, November 12-14, 2015, 2015