Matthew Riemer
24 papers · 2016–2026 · 7 venues · across top CS/AI venues
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Research topics
Artificial Intelligence
(22)
Core AI
(19)
Machine Learning
(13)
Reinforcement Learning
(10)
Methods
(10)
Learning Types Β· Machine Learning
(9)
Deep Learning
(5)
Natural Language Processing
(5)
Learning Paradigms
(4)
Application Areas
(3)
Learning Types Β· Deep Learning
(3)
Applications
(2)
Core Methods
(2)
Optimization & Theory
(2)
Resources & Methods
(2)
Keywords
reinforcement learning
(5)
large language model
(4)
hierarchical reinforcement learning
(4)
temporal abstraction
(4)
option learning
(3)
multi-agent reinforcement learning
(3)
catastrophic forgetting
(2)
continual learning
(2)
mixing time
(2)
experience replay
(2)
theory of mind
(2)
partially observable environment
(2)
policy gradient
(2)
average reward
(2)
natural language processing
(1)
transfer learning
(1)
real-time control
(1)
policy optimization
(1)
representation learning
(1)
online learning
(1)
Papers
AI Steerability 360: A Toolkit for Steering Large Language Models
ACL 2026
The Shepherd Test: How Will Super Intelligent Agents Balance Care and Control in Asymmetric Relationships?
AAAI 2026
Combining Domain and Alignment Vectors Provides Better Knowledge-Safety Trade-offs in LLMs
ACL 2025
Enabling Realtime Reinforcement Learning at Scale with Staggered Asynchronous Inference
ICLR 2025
EpMAN: Episodic Memory AttentioN for Generalizing to Longer Contexts
ACL 2025
Handling Delay in Real-Time Reinforcement Learning
ICLR 2025
Position: Theory of Mind Benchmarks are Broken for Large Language Models
ICML 2025
A Deep Dive into the Trade-Offs of Parameter-Efficient Preference Alignment Techniques
ACL 2024
Balancing Context Length and Mixing Times for Reinforcement Learning at Scale
NeurIPS 2024
ComVas: Contextual Moral Values Alignment System
IJCAI 2024
Context-Specific Representation Abstraction for Deep Option Learning
AAAI 2022
Continual Learning In Environments With Polynomial Mixing Times
NeurIPS 2022
Influencing Long-Term Behavior in Multiagent Reinforcement Learning
NeurIPS 2022
Efficient Black-Box Planning Using Macro-Actions with Focused Effects
IJCAI 2021
RL Generalization in a Theory of Mind Game Through a Sleep Metaphor (Student Abstract)
AAAI 2021
Hierarchical Average Reward Policy Gradient Algorithms (Student Abstract)
AAAI 2020
On the Role of Weight Sharing During Deep Option Learning
AAAI 2020
Learning to Learn without Forgetting by Maximizing Transfer and Minimizing Interference
ICLR 2019
Learning to Teach in Cooperative Multiagent Reinforcement Learning
AAAI 2019
Recursive Routing Networks: Learning to Compose Modules for Language Understanding
NAACL 2019
Scalable Recollections for Continual Lifelong Learning
AAAI 2019
Learning Abstract Options
NeurIPS 2018
Routing Networks: Adaptive Selection of Non-Linear Functions for Multi-Task Learning
ICLR 2018
Correcting Forecasts with Multifactor Neural Attention
ICML 2016