Domain Scoping for Subject Matter Experts. - Elham Khabiri; Matthew Riemer; Fenno F. Terry Heath III; Richard Hull 001 (AAAI Fall Symposia, 2015)

A Deep Learning and Knowledge Transfer Based Architecture for Social Media User Characteristic Determination. - Matthew Riemer; Sophia Krasikov; Harini Srinivasan (SocialNLP@NAACL, 2015)

Alexandria: Extensible Framework for Rapid Exploration of Social Media. - Fenno F. Terry Heath III; Richard Hull 001; Elham Khabiri; Matthew Riemer; Noi Sukaviriya; Roman Vaculín (BigData Congress, 2015)

Correcting Forecasts with Multifactor Neural Attention. - Matthew Riemer; Aditya Vempaty; Flávio P. Calmon; Fenno F. Terry Heath III; Richard Hull 001; Elham Khabiri (ICML, 2016)

Representation Stability as a Regularizer for Improved Text Analytics Transfer Learning. - Matthew Riemer; Elham Khabiri; Richard Goodwin (CoRR, 2017)

Generation and Consolidation of Recollections for Efficient Deep Lifelong Learning. - Matthew Riemer; Michele Franceschini; Tim Klinger (CoRR, 2017)

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

Learning Abstract Options. - Matthew Riemer; Miao Liu 001; Gerald Tesauro (NeurIPS, 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 to Teach in Cooperative Multiagent Reinforcement Learning. - Shayegan Omidshafiei; Dong-Ki Kim; Miao Liu 001; 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 001; Michele Franceschini (AAAI, 2019)

Learning to Learn without Forgetting by Maximizing Transfer and Minimizing Interference. - Matthew Riemer; Ignacio Cases; Robert Ajemian; Miao Liu 001; Irina Rish; Yuhai Tu; Gerald Tesauro (ICLR (Poster), 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 (NAACL-HLT (1), 2019)

Learning Hierarchical Teaching in Cooperative Multiagent Reinforcement Learning. - Dong-Ki Kim; Miao Liu 001; Shayegan Omidshafiei; Sebastian Lopez-Cot; Matthew Riemer; Golnaz Habibi; Gerald Tesauro; Sami Mourad; Murray Campbell; Jonathan P. How (CoRR, 2019)

Continual Learning with Self-Organizing Maps. - Pouya Bashivan; Martin Schrimpf; Robert Ajemian; Irina Rish; Matthew Riemer; Yuhai Tu (CoRR, 2019)

Routing Networks and the Challenges of Modular and Compositional Computation. - Clemens Rosenbaum; Ignacio Cases; Matthew Riemer; Tim Klinger (CoRR, 2019)

Hierarchical Average Reward Policy Gradient Algorithms. - Akshay Dharmavaram; Matthew Riemer; Shalabh Bhatnagar (CoRR, 2019)

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 001; Gerald Tesauro (AAAI, 2020)

Learning Hierarchical Teaching Policies for Cooperative Agents. - Dong-Ki Kim; Miao Liu 001; Shayegan Omidshafiei; Sebastian Lopez-Cot; Matthew Riemer; Golnaz Habibi; Gerald Tesauro; Sami Mourad; Murray Campbell; Jonathan P. How (AAMAS, 2020)

Finding Macro-Actions with Disentangled Effects for Efficient Planning with the Goal-Count Heuristic. - Cameron Allen; Tim Klinger; George Konidaris 001; 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)

Deep RL With Information Constrained Policies: Generalization in Continuous Control. - Tyler Malloy; Chris R. Sims; Tim Klinger; Miao Liu 001; Matthew Riemer; Gerald Tesauro (CoRR, 2020)

Consolidation via Policy Information Regularization in Deep RL for Multi-Agent Games. - Tyler Malloy; Tim Klinger; Miao Liu 001; Matthew Riemer; Gerald Tesauro; Chris R. Sims (CoRR, 2020)

RL Generalization in a Theory of Mind Game Through a Sleep Metaphor (Student Abstract). - Tyler Malloy; Tim Klinger; Miao Liu 001; Gerald Tesauro; Matthew Riemer; Chris R. Sims (AAAI, 2021)

Capacity-Limited Decentralized Actor-Critic for Multi-Agent Games. - Tyler Malloy; Chris R. Sims; Tim Klinger; Miao Liu 001; Matthew Riemer; Gerald Tesauro (CoG, 2021)

Modeling Capacity-Limited Decision Making Using a Variational Autoencoder. - Tyler Malloy; Tim Klinger; Miao Liu 001; Gerald Tesauro; Matthew Riemer; Chris R. Sims (CogSci, 2021)

A Policy Gradient Algorithm for Learning to Learn in Multiagent Reinforcement Learning. - Dong-Ki Kim; Miao Liu 001; Matthew Riemer; Chuangchuang Sun; Marwa Abdulhai; Golnaz Habibi; Sebastian Lopez-Cot; Gerald Tesauro; Jonathan P. How (ICML, 2021)

Efficient Black-Box Planning Using Macro-Actions with Focused Effects. - Cameron Allen; Michael Katz 001; Tim Klinger; George Konidaris 001; Matthew Riemer; Gerald Tesauro (IJCAI, 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; et al. (CoRR, 2021)

Context-Specific Representation Abstraction for Deep Option Learning. - Marwa Abdulhai; Dong-Ki Kim; Matthew Riemer; Miao Liu 001; Gerald Tesauro; Jonathan P. How (AAAI, 2022)

Influencing Long-Term Behavior in Multiagent Reinforcement Learning. - Dong-Ki Kim; Matthew Riemer; Miao Liu 001; Jakob N. Foerster; Michael Everett; Chuangchuang Sun; 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)

Game-Theoretical Perspectives on Active Equilibria: A Preferred Solution Concept over Nash Equilibria. - Dong-Ki Kim; Matthew Riemer; Miao Liu 001; Jakob N. Foerster; Gerald Tesauro; Jonathan P. How (CoRR, 2022)

Towards Continual Reinforcement Learning: A Review and Perspectives. - Khimya Khetarpal; Matthew Riemer; Irina Rish; Doina Precup (J. Artif. Intell. Res., 2022)

Learning in Factored Domains with Information-Constrained Visual Representations. - Tyler Malloy; Miao Liu 001; Matthew D. Riemer; Tim Klinger; Gerald Tesauro; Chris R. Sims (CoRR, 2023)

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 (ACL (1), 2024)

ComVas: Contextual Moral Values Alignment System. - Inkit Padhi; Pierre L. Dognin; Jesus Rios; Ronny Luss; Swapnaja Achintalwar; Matthew Riemer; Miao Liu 001; Prasanna Sattigeri; Manish Nagireddy; Kush R. Varshney; Djallel Bouneffouf 001 (IJCAI, 2024)

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

Contextual Moral Value Alignment Through Context-Based Aggregation. - Pierre L. Dognin; Jesus Rios; Ronny Luss; Inkit Padhi; Matthew D. Riemer; Miao Liu 001; Prasanna Sattigeri; Manish Nagireddy; Kush R. Varshney; Djallel Bouneffouf 001 (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)

Can Large Language Models Adapt to Other Agents In-Context? - Matthew Riemer; Zahra Ashktorab; Djallel Bouneffouf 001; Payel Das; Miao Liu 001; Justin D. Weisz; Murray Campbell (CoRR, 2024)

EpMAN: Episodic Memory AttentioN for Generalizing to Longer Contexts. - Subhajit Chaudhury; Payel Das; Sarathkrishna Swaminathan; Georgios Kollias; Elliot Nelson; Khushbu Pahwa; Tejaswini Pedapati; Igor Melnyk; Matthew Riemer (ACL (1), 2025)

Contextual Value Alignment. - Pierre Dognin; Jesus Rios; Ronny Luss; Prasanna Sattigeri; Miao Liu 001; Inkit Padhi; Matthew Riemer; Manish Nagireddy; Kush R. Varshney; Djallel Bouneffouf 001 (ICASSP, 2025)

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

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

Scopes of Alignment. - Kush R. Varshney; Zahra Ashktorab; Djallel Bouneffouf 001; Matthew Riemer; Justin D. Weisz (CoRR, 2025)

Agentic AI Needs a Systems Theory. - Erik Miehling; Karthikeyan Natesan Ramamurthy; Kush R. Varshney; Matthew Riemer; Djallel Bouneffouf 001; John T. Richards; Amit Dhurandhar; Elizabeth M. Daly; Michael Hind; Prasanna Sattigeri; Dennis Wei; Ambrish Rawat; et al. (CoRR, 2025)

The Ultimate Test of Superintelligent AI Agents: Can an AI Balance Care and Control in Asymmetric Relationships? - Djallel Bouneffouf 001; Matthew Riemer; Kush R. Varshney (CoRR, 2025)