Riemer, Matthew

25 publications

ICLR 2025 Enabling Realtime Reinforcement Learning at Scale with Staggered Asynchronous Inference Matthew Riemer, Gopeshh Subbaraj, Glen Berseth, Irina Rish

ICLR 2025 Handling Delay in Real-Time Reinforcement Learning Ivan Anokhin, Rishav Rishav, Matthew Riemer, Stephen Chung, Irina Rish, Samira Ebrahimi Kahou

ICML 2025 Position: Theory of Mind Benchmarks Are Broken for Large Language Models Matthew Riemer, Zahra Ashktorab, Djallel Bouneffouf, Payel Das, Miao Liu, Justin D. Weisz, Murray Campbell

NeurIPS 2024 Balancing Context Length and Mixing Times for Reinforcement Learning at Scale Matthew Riemer, Khimya Khetarpal, Janarthanan Rajendran, Sarath Chandar

IJCAI 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

NeurIPSW 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

ICMLW 2024 Realtime Reinforcement Learning: Towards Rapid Asynchronous Deployment of Large Models Matthew Riemer, Gopeshh Subbaraj, Glen Berseth, Irina Rish

ICMLW 2024 Scalable Approaches for a Theory of Many Minds Maximilian Puelma Touzel, Amin Memarian, Matthew Riemer, Andrei Mircea, Andrew Robert Williams, Elin Ahlstrand, Lucas Lehnert, Rupali Bhati, Guillaume Dumas, Irina Rish

AAAI 2022 Context-Specific Representation Abstraction for Deep Option Learning Marwa Abdulhai, Dong-Ki Kim, Matthew Riemer, Miao Liu, Gerald Tesauro, Jonathan P. How

NeurIPS 2022 Continual Learning in Environments with Polynomial Mixing Times Matthew Riemer, Sharath Chandra Raparthy, Ignacio Cases, Gopeshh Subbaraj, Maximilian Puelma Touzel, Irina Rish

NeurIPS 2022 Influencing Long-Term Behavior in Multiagent Reinforcement Learning Dong-Ki Kim, Matthew Riemer, Miao Liu, Jakob Foerster, Michael Everett, Chuangchuang Sun, Gerald Tesauro, Jonathan P How

ICLRW 2022 Influencing Long-Term Behavior in Multiagent Reinforcement Learning Dong-Ki Kim, Matthew Riemer, Miao Liu, Jakob Nicolaus Foerster, Michael Everett, Chuangchuang Sun, Gerald Tesauro, Jonathan P How

NeurIPSW 2022 Learning in Factored Domains with Information-Constrained Visual Representations Tailia Malloy, Chris R Sims, Tim Klinger, Matthew Riemer, Miao Liu, Gerald Tesauro

ICLRW 2022 Summarizing Societies: Agent Abstraction in Multi-Agent Reinforcement Learning Amin Memarian, Maximilian Puelma Touzel, Matthew Riemer, Rupali Bhati, Irina Rish

JAIR 2022 Towards Continual Reinforcement Learning: A Review and Perspectives Khimya Khetarpal, Matthew Riemer, Irina Rish, Doina Precup

IJCAI 2021 Efficient Black-Box Planning Using Macro-Actions with Focused Effects Cameron Allen, Michael Katz, Tim Klinger, George Konidaris, Matthew Riemer, Gerald Tesauro

AAAI 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

AAAI 2020 Hierarchical Average Reward Policy Gradient Algorithms (Student Abstract) Akshay Dharmavaram, Matthew Riemer, Shalabh Bhatnagar

AAAI 2020 On the Role of Weight Sharing During Deep Option Learning Matthew Riemer, Ignacio Cases, Clemens Rosenbaum, Miao Liu, Gerald Tesauro

ICLR 2019 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

AAAI 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

AAAI 2019 Scalable Recollections for Continual Lifelong Learning Matthew Riemer, Tim Klinger, Djallel Bouneffouf, Michele Franceschini

NeurIPS 2018 Learning Abstract Options Matthew Riemer, Miao Liu, Gerald Tesauro

ICLR 2018 Routing Networks: Adaptive Selection of Non-Linear Functions for Multi-Task Learning Clemens Rosenbaum, Tim Klinger, Matthew Riemer

ICML 2016 Correcting Forecasts with Multifactor Neural Attention Matthew Riemer, Aditya Vempaty, Flavio Calmon, Fenno Heath, Richard Hull, Elham Khabiri