123 citations · 406 across the 18 of their papers we have counts for
20 papers
Showing versus Doing: Teaching by Demonstration
Mark K. Ho, Michael L. Littman, James MacGlashan +2
People often learn from others' demonstrations, and inverse reinforcement learning (IRL) techniques have realized this capacity in machines. In contrast, teaching by demonstration…
Structurally guided task decomposition in spatial navigation tasks
Ruiqi He, Carlos G. Correa, Thomas L. Griffiths +1
How are people able to plan so efficiently despite limited cognitive resources? We aimed to answer this question by extending an existing model of human task decomposition that can…
Diagnosis, Feedback, Adaptation: A Human-in-the-Loop Framework for Test-Time Policy Adaptation
Andi Peng, Aviv Netanyahu, Mark Ho +4
Policies often fail due to distribution shift -- changes in the state and reward that occur when a policy is deployed in new environments. Data augmentation can increase robustness…
Bayesian Reinforcement Learning with Limited Cognitive Load
Dilip Arumugam, Mark K. Ho, Noah D. Goodman +1
All biological and artificial agents must learn and make decisions given limits on their ability to process information. As such, a general theory of adaptive behavior should be ab…
Humans decompose tasks by trading off utility and computational cost
Carlos G. Correa, Mark K. Ho, Frederick Callaway +2
Human behavior emerges from planning over elaborate decompositions of tasks into goals, subgoals, and low-level actions. How are these decompositions created and used? Here, we pro…
On Rate-Distortion Theory in Capacity-Limited Cognition & Reinforcement Learning
Dilip Arumugam, Mark K. Ho, Noah D. Goodman +1
Throughout the cognitive-science literature, there is widespread agreement that decision-making agents operating in the real world do so under limited information-processing capabi…