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2020 – today
- 2025
[i31]Kush R. Varshney, Zahra Ashktorab, Djallel Bouneffouf, Matthew Riemer, Justin D. Weisz:
Scopes of Alignment. CoRR abs/2501.12405 (2025)- 2024
- [c24]Megh Thakkar, Quentin Fournier, Matthew Riemer, Pin-Yu Chen, Amal Zouaq, Payel Das, Sarath Chandar:
A Deep Dive into the Trade-Offs of Parameter-Efficient Preference Alignment Techniques. ACL (1) 2024: 5732-5745 - [c23]Inkit Padhi, Pierre L. Dognin, Jesus Rios, Ronny Luss, Swapnaja Achintalwar, Matthew Riemer, Miao Liu, Prasanna Sattigeri, Manish Nagireddy, Kush R. Varshney, Djallel Bouneffouf:
ComVas: Contextual Moral Values Alignment System. IJCAI 2024: 8759-8762 - [c22]Matthew Riemer, Khimya Khetarpal, Janarthanan Rajendran, Sarath Chandar:
Balancing Context Length and Mixing Times for Reinforcement Learning at Scale. NeurIPS 2024 - [i30]Pierre L. Dognin, Jesus Rios, Ronny Luss, Inkit Padhi, Matthew D. Riemer, Miao Liu, Prasanna Sattigeri, Manish Nagireddy, Kush R. Varshney, Djallel Bouneffouf:
Contextual Moral Value Alignment Through Context-Based Aggregation. CoRR abs/2403.12805 (2024) - [i29]Megh Thakkar, Quentin Fournier, Matthew D. Riemer, Pin-Yu Chen, Amal Zouaq, Payel Das, Sarath Chandar:
A Deep Dive into the Trade-Offs of Parameter-Efficient Preference Alignment Techniques. CoRR abs/2406.04879 (2024) - [i28]Megh Thakkar, Yash More, Quentin Fournier, Matthew Riemer, Pin-Yu Chen, Amal Zouaq, Payel Das, Sarath Chandar:
Combining Domain and Alignment Vectors to Achieve Better Knowledge-Safety Trade-offs in LLMs. CoRR abs/2411.06824 (2024) - [i27]Matthew Riemer, Gopeshh Subbaraj, Glen Berseth, Irina Rish:
Enabling Realtime Reinforcement Learning at Scale with Staggered Asynchronous Inference. CoRR abs/2412.14355 (2024) - [i26]Matthew Riemer, Zahra Ashktorab, Djallel Bouneffouf, Payel Das, Miao Liu, Justin D. Weisz, Murray Campbell:
Can Large Language Models Adapt to Other Agents In-Context? CoRR abs/2412.19726 (2024) - 2023
- [i25]Tyler Malloy, Miao Liu, Matthew D. Riemer, Tim Klinger, Gerald Tesauro, Chris R. Sims:
Learning in Factored Domains with Information-Constrained Visual Representations. CoRR abs/2303.17508 (2023) - 2022
- [j1]Khimya Khetarpal, Matthew Riemer, Irina Rish, Doina Precup:
Towards Continual Reinforcement Learning: A Review and Perspectives. J. Artif. Intell. Res. 75: 1401-1476 (2022) - [c21]Marwa Abdulhai, Dong-Ki Kim, Matthew Riemer, Miao Liu, Gerald Tesauro, Jonathan P. How:
Context-Specific Representation Abstraction for Deep Option Learning. AAAI 2022: 5959-5967 - [c20]Dong-Ki Kim, Matthew Riemer, Miao Liu, Jakob N. Foerster, Michael Everett, Chuangchuang Sun, Gerald Tesauro, Jonathan P. How:
Influencing Long-Term Behavior in Multiagent Reinforcement Learning. NeurIPS 2022 - [c19]Matthew Riemer, Sharath Chandra Raparthy, Ignacio Cases, Gopeshh Subbaraj, Maximilian Puelma Touzel, Irina Rish:
Continual Learning In Environments With Polynomial Mixing Times. NeurIPS 2022 - [i24]Dong-Ki Kim, Matthew Riemer, Miao Liu, Jakob N. Foerster, Michael Everett, Chuangchuang Sun, Gerald Tesauro, Jonathan P. How:
Influencing Long-Term Behavior in Multiagent Reinforcement Learning. CoRR abs/2203.03535 (2022) - [i23]Dong-Ki Kim, Matthew Riemer, Miao Liu, Jakob N. Foerster, Gerald Tesauro, Jonathan P. How:
Game-Theoretical Perspectives on Active Equilibria: A Preferred Solution Concept over Nash Equilibria. CoRR abs/2210.16175 (2022) - 2021
- [c18]Tyler Malloy, Tim Klinger, Miao Liu, Gerald Tesauro, Matthew Riemer, Chris R. Sims:
RL Generalization in a Theory of Mind Game Through a Sleep Metaphor (Student Abstract). AAAI 2021: 15841-15842 - [c17]Tyler Malloy, Chris R. Sims
, Tim Klinger, Miao Liu, Matthew Riemer, Gerald Tesauro:
Capacity-Limited Decentralized Actor-Critic for Multi-Agent Games. CoG 2021: 1-8 - [c16]Tyler Malloy, Tim Klinger, Miao Liu, Gerald Tesauro, Matthew Riemer, Chris R. Sims:
Modeling Capacity-Limited Decision Making Using a Variational Autoencoder. CogSci 2021 - [c15]Dong-Ki Kim, Miao Liu, Matthew Riemer, Chuangchuang Sun, Marwa Abdulhai, Golnaz Habibi, Sebastian Lopez-Cot, Gerald Tesauro, Jonathan P. How:
A Policy Gradient Algorithm for Learning to Learn in Multiagent Reinforcement Learning. ICML 2021: 5541-5550 - [c14]Cameron Allen, Michael Katz, Tim Klinger, George Konidaris, Matthew Riemer, Gerald Tesauro:
Efficient Black-Box Planning Using Macro-Actions with Focused Effects. IJCAI 2021: 4024-4031 - [i22]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:
Sequoia: A Software Framework to Unify Continual Learning Research. CoRR abs/2108.01005 (2021) - [i21]Marwa Abdulhai, Dong-Ki Kim, Matthew Riemer, Miao Liu, Gerald Tesauro, Jonathan P. How:
Context-Specific Representation Abstraction for Deep Option Learning. CoRR abs/2109.09876 (2021) - [i20]Matthew Riemer, Sharath Chandra Raparthy, Ignacio Cases, Gopeshh Subbaraj, Maximilian Puelma Touzel, Irina Rish:
Continual Learning In Environments With Polynomial Mixing Times. CoRR abs/2112.07066 (2021) - 2020
- [c13]Matthew Riemer, Ignacio Cases, Clemens Rosenbaum, Miao Liu, Gerald Tesauro:
On the Role of Weight Sharing During Deep Option Learning. AAAI 2020: 5519-5526 - [c12]Akshay Dharmavaram, Matthew Riemer, Shalabh Bhatnagar:
Hierarchical Average Reward Policy Gradient Algorithms (Student Abstract). AAAI 2020: 13777-13778 - [c11]Dong-Ki Kim, Miao Liu, Shayegan Omidshafiei, Sebastian Lopez-Cot, Matthew Riemer, Golnaz Habibi, Gerald Tesauro, Sami Mourad, Murray Campbell, Jonathan P. How:
Learning Hierarchical Teaching Policies for Cooperative Agents. AAMAS 2020: 620-628 - [i19]Cameron Allen, Tim Klinger, George Konidaris, Matthew Riemer, Gerald Tesauro:
Finding Macro-Actions with Disentangled Effects for Efficient Planning with the Goal-Count Heuristic. CoRR abs/2004.13242 (2020) - [i18]Tim Klinger, Dhaval Adjodah, Vincent Marois, Josh Joseph, Matthew Riemer, Alex 'Sandy' Pentland, Murray Campbell:
A Study of Compositional Generalization in Neural Models. CoRR abs/2006.09437 (2020) - [i17]Tyler Malloy, Chris R. Sims, Tim Klinger, Miao Liu, Matthew Riemer, Gerald Tesauro:
Deep RL With Information Constrained Policies: Generalization in Continuous Control. CoRR abs/2010.04646 (2020) - [i16]Dong-Ki Kim, Miao Liu, Matthew Riemer, Chuangchuang Sun, Marwa Abdulhai, Golnaz Habibi, Sebastian Lopez-Cot, Gerald Tesauro, Jonathan P. How:
A Policy Gradient Algorithm for Learning to Learn in Multiagent Reinforcement Learning. CoRR abs/2011.00382 (2020) - [i15]Tyler Malloy, Tim Klinger, Miao Liu, Matthew Riemer, Gerald Tesauro, Chris R. Sims:
Consolidation via Policy Information Regularization in Deep RL for Multi-Agent Games. CoRR abs/2011.11517 (2020) - [i14]Khimya Khetarpal, Matthew Riemer, Irina Rish, Doina Precup:
Towards Continual Reinforcement Learning: A Review and Perspectives. CoRR abs/2012.13490 (2020)
2010 – 2019
- 2019
- [c10]Matthew Riemer, Tim Klinger, Djallel Bouneffouf, Michele Franceschini:
Scalable Recollections for Continual Lifelong Learning. AAAI 2019: 1352-1359 - [c9]Shayegan Omidshafiei, Dong-Ki Kim, Miao Liu, Gerald Tesauro, Matthew Riemer, Christopher Amato, Murray Campbell, Jonathan P. How:
Learning to Teach in Cooperative Multiagent Reinforcement Learning. AAAI 2019: 6128-6136 - [c8]Matthew Riemer, Ignacio Cases, Robert Ajemian, Miao Liu, Irina Rish, Yuhai Tu, Gerald Tesauro:
Learning to Learn without Forgetting by Maximizing Transfer and Minimizing Interference. ICLR (Poster) 2019 - [c7]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:
Recursive Routing Networks: Learning to Compose Modules for Language Understanding. NAACL-HLT (1) 2019: 3631-3648 - [i13]Dong-Ki Kim, Miao Liu, Shayegan Omidshafiei, Sebastian Lopez-Cot, Matthew Riemer, Golnaz Habibi, Gerald Tesauro, Sami Mourad, Murray Campbell, Jonathan P. How:
Learning Hierarchical Teaching in Cooperative Multiagent Reinforcement Learning. CoRR abs/1903.03216 (2019) - [i12]Pouya Bashivan, Martin Schrimpf, Robert Ajemian, Irina Rish, Matthew Riemer, Yuhai Tu:
Continual Learning with Self-Organizing Maps. CoRR abs/1904.09330 (2019) - [i11]Clemens Rosenbaum, Ignacio Cases, Matthew Riemer, Tim Klinger:
Routing Networks and the Challenges of Modular and Compositional Computation. CoRR abs/1904.12774 (2019) - [i10]Akshay Dharmavaram, Matthew Riemer, Shalabh Bhatnagar:
Hierarchical Average Reward Policy Gradient Algorithms. CoRR abs/1911.08826 (2019) - [i9]Matthew Riemer, Ignacio Cases, Clemens Rosenbaum, Miao Liu, Gerald Tesauro:
On the Role of Weight Sharing During Deep Option Learning. CoRR abs/1912.13408 (2019) - 2018
- [c6]Clemens Rosenbaum, Tim Klinger, Matthew Riemer:
Routing Networks: Adaptive Selection of Non-Linear Functions for Multi-Task Learning. ICLR (Poster) 2018 - [c5]Matthew Riemer, Miao Liu, Gerald Tesauro:
Learning Abstract Options. NeurIPS 2018: 10445-10455 - [i8]Shayegan Omidshafiei, Dong-Ki Kim, Miao Liu, Gerald Tesauro, Matthew Riemer, Christopher Amato, Murray Campbell, Jonathan P. How:
Learning to Teach in Cooperative Multiagent Reinforcement Learning. CoRR abs/1805.07830 (2018) - [i7]Payel Das, Kahini Wadhawan, Oscar Chang, Tom Sercu, Cícero Nogueira dos Santos, Matthew Riemer, Inkit Padhi, Vijil Chenthamarakshan, Aleksandra Mojsilovic:
PepCVAE: Semi-Supervised Targeted Design of Antimicrobial Peptide Sequences. CoRR abs/1810.07743 (2018) - [i6]Matthew Riemer, Miao Liu, Gerald Tesauro:
Learning Abstract Options. CoRR abs/1810.11583 (2018) - [i5]Matthew Riemer, Ignacio Cases, Robert Ajemian, Miao Liu, Irina Rish, Yuhai Tu, Gerald Tesauro:
Learning to Learn without Forgetting By Maximizing Transfer and Minimizing Interference. CoRR abs/1810.11910 (2018) - 2017
- [i4]Matthew Riemer, Elham Khabiri, Richard Goodwin:
Representation Stability as a Regularizer for Improved Text Analytics Transfer Learning. CoRR abs/1704.03617 (2017) - [i3]Clemens Rosenbaum, Tim Klinger, Matthew Riemer:
Routing Networks: Adaptive Selection of Non-linear Functions for Multi-Task Learning. CoRR abs/1711.01239 (2017) - [i2]Matthew Riemer, Michele Franceschini, Tim Klinger:
Generation and Consolidation of Recollections for Efficient Deep Lifelong Learning. CoRR abs/1711.06761 (2017) - 2016
- [c4]Matthew Riemer, Aditya Vempaty, Flávio P. Calmon, Fenno F. Terry Heath III, Richard Hull, Elham Khabiri:
Correcting Forecasts with Multifactor Neural Attention. ICML 2016: 3010-3019 - 2015
- [c3]Elham Khabiri, Matthew Riemer, Fenno F. Terry Heath III, Richard Hull:
Domain Scoping for Subject Matter Experts. AAAI Fall Symposia 2015: 30-36 - [c2]Matthew Riemer, Sophia Krasikov, Harini Srinivasan:
A Deep Learning and Knowledge Transfer Based Architecture for Social Media User Characteristic Determination. SocialNLP@NAACL 2015: 39-47 - [c1]Fenno F. Terry Heath III, Richard Hull, Elham Khabiri, Matthew Riemer, Noi Sukaviriya, Roman Vaculín:
Alexandria: Extensible Framework for Rapid Exploration of Social Media. BigData Congress 2015: 483-490 - [i1]Fenno F. Terry Heath III, Richard Hull, Elham Khabiri, Matthew Riemer, Noi Sukaviriya, Roman Vaculín:
Alexandria: Extensible Framework for Rapid Exploration of Social Media. CoRR abs/1507.06667 (2015)
Coauthor Index
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last updated on 2025-02-26 21:44 CET by the dblp team
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