Mark K. Ho

Publication Activity (10 Years)

Years Active: 2013-2024
Publications (10 Years): 51

Top Topics

Active Learning

Reinforcement Learning

Guided Exploration

Top Venues

Publications

Maya Malaviya, Mark K. Ho
Teaching Functions with Gaussian Process Regression. AAAI Spring Symposia (2024)
David Abel, Mark K. Ho, Anna Harutyunyan
Three Dogmas of Reinforcement Learning. CoRR (2024)
Ilia Sucholutsky, Katherine M. Collins, Maya Malaviya, Nori Jacoby, Weiyang Liu, Theodore R. Sumers, Michalis Korakakis, Umang Bhatt, Mark K. Ho, Joshua B. Tenenbaum, Bradley C. Love, Zachary A. Pardos, Adrian Weller, Thomas L. Griffiths
Representational Alignment Supports Effective Machine Teaching. CoRR (2024)
Ruiqi He, Carlos G. Correa, Tom Griffiths, Mark K. Ho
Structurally Guided Task Decomposition in Spatial Navigation Tasks (Student Abstract). AAAI (2024)
Ruiqi He, Carlos G. Correa, Thomas L. Griffiths, Mark K. Ho
Structurally guided task decomposition in spatial navigation tasks. CoRR (2023)
Carlos G. Correa, Mark K. Ho, Frederick Callaway, Nathaniel D. Daw, Thomas L. Griffiths
Humans decompose tasks by trading off utility and computational cost. PLoS Comput. Biol. 19 (6) (2023)
Andi Peng, Aviv Netanyahu, Mark K. Ho, Tianmin Shu, Andreea Bobu, Julie Shah, Pulkit Agrawal
Diagnosis, Feedback, Adaptation: A Human-in-the-Loop Framework for Test-Time Policy Adaptation. ICML (2023)
Dilip Arumugam, Mark K. Ho, Noah D. Goodman, Benjamin Van Roy
Bayesian Reinforcement Learning with Limited Cognitive Load. CoRR (2023)
Andi Peng, Aviv Netanyahu, Mark K. Ho, Tianmin Shu, Andreea Bobu, Julie Shah, Pulkit Agrawal
Diagnosis, Feedback, Adaptation: A Human-in-the-Loop Framework for Test-Time Policy Adaptation. CoRR (2023)
Carlos G. Correa, Sophia Sanborn, Mark K. Ho, Frederick Callaway, Nathaniel D. Daw, Thomas L. Griffiths
Exploring the hierarchical structure of human plans via program generation. CoRR (2023)
Sunayana Rane, Mark K. Ho, Ilia Sucholutsky, Thomas L. Griffiths
Concept Alignment as a Prerequisite for Value Alignment. CoRR (2023)
David Abel, Will Dabney, Anna Harutyunyan, Mark K. Ho, Michael L. Littman, Doina Precup, Satinder Singh
On the Expressivity of Markov Reward (Extended Abstract). IJCAI (2022)
Mark K. Ho, Thomas L. Griffiths
Cognitive Science as a Source of Forward and Inverse Models of Human Decisions for Robotics and Control. Annu. Rev. Control. Robotics Auton. Syst. 5 (2022)
Dilip Arumugam, Mark K. Ho, Noah D. Goodman, Benjamin Van Roy
On Rate-Distortion Theory in Capacity-Limited Cognition & Reinforcement Learning. CoRR (2022)
Theodore R. Sumers, Robert D. Hawkins, Mark K. Ho, Thomas L. Griffiths, Dylan Hadfield-Menell
Linguistic communication as (inverse) reward design. CoRR (2022)
Theodore R. Sumers, Robert D. Hawkins, Mark K. Ho, Thomas L. Griffiths, Dylan Hadfield-Menell
How to talk so your robot will learn: Instructions, descriptions, and pragmatics. CoRR (2022)
Theodore R. Sumers, Robert D. Hawkins, Mark K. Ho, Tom Griffiths, Dylan Hadfield-Menell
How to talk so AI will learn: Instructions, descriptions, and autonomy. NeurIPS (2022)
Carlos G. Correa, Mark K. Ho, Frederick Callaway, Nathaniel D. Daw, Thomas L. Griffiths
Humans decompose tasks by trading off utility and computational cost. CoRR (2022)
Charley M. Wu, Mark K. Ho, Benjamin Kahl, Christina Leuker, Björn Meder, Ralf H. J. M. Kurvers
Specialization and selective social attention establishes the balance between individual and social learning. CogSci (2021)
Theodore R. Sumers, Robert X. D. Hawkins, Mark K. Ho, Thomas L. Griffiths
Extending rational models of communication from beliefs to actions. CoRR (2021)
Yun-Shiuan Chuang, Xuezhou Zhang, Yuzhe Ma, Mark K. Ho, Joseph L. Austerweil, Jerry Zhu
Using Machine Teaching to Investigate Human Assumptions when Teaching Reinforcement Learners. CogSci (2021)
David Abel, Will Dabney, Anna Harutyunyan, Mark K. Ho, Michael L. Littman, Doina Precup, Satinder Singh
On the Expressivity of Markov Reward. CoRR (2021)
Theodore R. Sumers, Mark K. Ho, Robert X. D. Hawkins, Karthik Narasimhan, Thomas L. Griffiths
Learning Rewards From Linguistic Feedback. AAAI (2021)
Mark K. Ho, Thomas L. Griffiths
Cognitive science as a source of forward and inverse models of human decisions for robotics and control. CoRR (2021)
Mark K. Ho, David Abel, Carlos G. Correa, Michael L. Littman, Jonathan D. Cohen, Thomas L. Griffiths
Control of mental representations in human planning. CoRR (2021)
Theodore R. Sumers, Robert D. Hawkins, Mark K. Ho, Tom Griffiths
Extending rational models of communication from beliefs to actions. CogSci (2021)
David Abel, Will Dabney, Anna Harutyunyan, Mark K. Ho, Michael L. Littman, Doina Precup, Satinder Singh
On the Expressivity of Markov Reward. NeurIPS (2021)
Guan Wang, Carl Trimbach, Jun Ki Lee, Mark K. Ho, Michael L. Littman
Teaching a Robot Tasks of Arbitrary Complexity via Human Feedback. HRI (2020)
Mark K. Ho, David Abel, Jonathan D. Cohen, Michael L. Littman, Thomas L. Griffiths
The Efficiency of Human Cognition Reflects Planned Information Processing. CoRR (2020)
Theodore R. Sumers, Mark K. Ho, Robert X. D. Hawkins, Karthik Narasimhan, Thomas L. Griffiths
Learning Rewards from Linguistic Feedback. CoRR (2020)
Carlos G. Correa, Mark K. Ho, Fred Callaway, Thomas L. Griffiths
Resource-rational Task Decomposition to Minimize Planning Costs. CoRR (2020)
Charley M. Wu, Natalia Vélez, Mark K. Ho, Robert L. Goldstone
Cognition, Collectives, and Human Culture. CogSci (2020)
Carlos G. Correa, Mark K. Ho, Frederick Callaway, Tom Griffiths
Resource-rational Task Decomposition to Minimize Planning Costs. CogSci (2020)
Arunima Sarin, Mark K. Ho, Justin Martin, Fiery Cushman
Punishment: Incentive or Communication? CogSci (2020)
Theodore R. Sumers, Mark K. Ho, Thomas L. Griffiths
Show or Tell? Demonstration is More Robust to Changes in Shared Perception than Explanation. CoRR (2020)
Mark K. Ho, David Abel, Jonathan D. Cohen, Michael L. Littman, Thomas L. Griffiths
People Do Not Just Plan, They Plan to Plan. AAAI (2020)
Yun-Shiuan Chuang, Xuezhou Zhang, Yuzhe Ma, Mark K. Ho, Joseph L. Austerweil, Xiaojin Zhu
Using Machine Teaching to Investigate Human Assumptions when Teaching Reinforcement Learners. CoRR (2020)
Theodore R. Sumers, Mark K. Ho, Tom Griffiths
Show or Tell? Demonstration is More Robust to Changes in Shared Perception than Explanation. CogSci (2020)
Carlos G. Correa, Frederick Callaway, Mark K. Ho, Tom Griffiths
Compositional subgoal representations. CogSci (2019)
Mark K. Ho, Joanna Korman, Thomas L. Griffiths
The Computational Structure of Unintentional Meaning. CoRR (2019)
Mark K. Ho, Joanna Korman, Tom Griffiths
The Computational Structure of Unintentional Meaning. CogSci (2019)
Micah Carroll, Rohin Shah, Mark K. Ho, Thomas L. Griffiths, Sanjit A. Seshia, Pieter Abbeel, Anca D. Dragan
On the Utility of Learning about Humans for Human-AI Coordination. CoRR (2019)
Micah Carroll, Rohin Shah, Mark K. Ho, Tom Griffiths, Sanjit A. Seshia, Pieter Abbeel, Anca D. Dragan
On the Utility of Learning about Humans for Human-AI Coordination. NeurIPS (2019)
Marcell Vazquez-Chanlatte, Susmit Jha, Ashish Tiwari, Mark K. Ho, Sanjit A. Seshia
Learning Task Specifications from Demonstrations. NeurIPS (2018)
Mark K. Ho, Michael L. Littman, Fiery Cushman, Joseph L. Austerweil
Effectively Learning from Pedagogical Demonstrations. CogSci (2018)
James MacGlashan, Mark K. Ho, Robert Tyler Loftin, Bei Peng, Guan Wang, David L. Roberts, Matthew E. Taylor, Michael L. Littman
Interactive Learning from Policy-Dependent Human Feedback. ICML (2017)
James MacGlashan, Mark K. Ho, Robert Tyler Loftin, Bei Peng, David L. Roberts, Matthew E. Taylor, Michael L. Littman
Interactive Learning from Policy-Dependent Human Feedback. CoRR (2017)
Mark K. Ho, Michael L. Littman, Joseph L. Austerweil
Teaching by Intervention: Working Backwards, Undoing Mistakes, or Correcting Mistakes? CogSci (2017)
Mark K. Ho, James MacGlashan, Amy Greenwald, Michael L. Littman, Elizabeth Hilliard, Carl Trimbach, Stephen Brawner, Josh Tenenbaum, Max Kleiman-Weiner, Joseph L. Austerweil
Feature-based Joint Planning and Norm Learning in Collaborative Games. CogSci (2016)
Mark K. Ho, Michael L. Littman, James MacGlashan, Fiery Cushman, Joseph L. Austerweil
Showing versus doing: Teaching by demonstration. NIPS (2016)
Max Kleiman-Weiner, Mark K. Ho, Joseph L. Austerweil, Michael L. Littman, Josh Tenenbaum
Coordinate to cooperate or compete: Abstract goals and joint intentions in social interaction. CogSci (2016)
Mark K. Ho, Michael L. Littman, Fiery Cushman, Joseph L. Austerweil
Teaching with Rewards and Punishments: Reinforcement or Communication? CogSci (2015)
Mark K. Ho, Fiery Cushman
Working Memory and Abstract Representation in the Context of Culture. CogSci (2013)