Working Memory and Abstract Representation in the Context of Culture. - Mark K. Ho; Fiery Cushman (CogSci, 2013)

Teaching with Rewards and Punishments: Reinforcement or Communication? - Mark K. Ho; Michael L. Littman; Fiery Cushman; Joseph L. Austerweil (CogSci, 2015)

Feature-based Joint Planning and Norm Learning in Collaborative Games. - Mark K. Ho; James MacGlashan; Amy Greenwald; Michael L. Littman; Elizabeth Hilliard; Carl Trimbach; Stephen Brawner; Josh Tenenbaum 001; Max Kleiman-Weiner; Joseph L. Austerweil (CogSci, 2016)

Coordinate to cooperate or compete: Abstract goals and joint intentions in social interaction. - Max Kleiman-Weiner; Mark K. Ho; Joseph L. Austerweil; Michael L. Littman; Josh Tenenbaum 001 (CogSci, 2016)

Showing versus doing: Teaching by demonstration. - Mark K. Ho; Michael L. Littman; James MacGlashan; Fiery Cushman; Joseph L. Austerweil (NIPS, 2016)

Teaching by Intervention: Working Backwards, Undoing Mistakes, or Correcting Mistakes? - Mark K. Ho; Michael L. Littman; Joseph L. Austerweil (CogSci, 2017)

Interactive Learning from Policy-Dependent Human Feedback. - James MacGlashan; Mark K. Ho; Robert Tyler Loftin; Bei Peng 001; Guan Wang; David L. Roberts 001; Matthew E. Taylor; Michael L. Littman (ICML, 2017)

Interactive Learning from Policy-Dependent Human Feedback. - James MacGlashan; Mark K. Ho; Robert Tyler Loftin; Bei Peng 001; David L. Roberts 001; Matthew E. Taylor; Michael L. Littman (CoRR, 2017)

Effectively Learning from Pedagogical Demonstrations. - Mark K. Ho; Michael L. Littman; Fiery Cushman; Joseph L. Austerweil (CogSci, 2018)

Learning Task Specifications from Demonstrations. - Marcell Vazquez-Chanlatte; Susmit Jha; Ashish Tiwari 001; Mark K. Ho; Sanjit A. Seshia (NeurIPS, 2018)

Compositional subgoal representations. - Carlos G. Correa; Frederick Callaway; Mark K. Ho; Tom Griffiths 001 (CogSci, 2019)

The Computational Structure of Unintentional Meaning. - Mark K. Ho; Joanna Korman; Tom Griffiths 001 (CogSci, 2019)

On the Utility of Learning about Humans for Human-AI Coordination. - Micah Carroll; Rohin Shah; Mark K. Ho; Tom Griffiths 001; Sanjit A. Seshia; Pieter Abbeel; Anca D. Dragan (NeurIPS, 2019)

People Do Not Just Plan, They Plan to Plan. - Mark K. Ho; David Abel; Jonathan D. Cohen 003; Michael L. Littman; Thomas L. Griffiths 001 (AAAI, 2020)

Resource-rational Task Decomposition to Minimize Planning Costs. - Carlos G. Correa; Mark K. Ho; Frederick Callaway; Tom Griffiths 001 (CogSci, 2020)

Punishment: Incentive or Communication? - Arunima Sarin; Mark K. Ho; Justin Martin; Fiery Cushman (CogSci, 2020)

Show or Tell? Demonstration is More Robust to Changes in Shared Perception than Explanation. - Theodore R. Sumers; Mark K. Ho; Tom Griffiths 001 (CogSci, 2020)

Cognition, Collectives, and Human Culture. - Charley M. Wu; Natalia Vélez; Mark K. Ho; Robert L. Goldstone (CogSci, 2020)

Teaching a Robot Tasks of Arbitrary Complexity via Human Feedback. - Guan Wang; Carl Trimbach; Jun Ki Lee; Mark K. Ho; Michael L. Littman (HRI, 2020)

The Efficiency of Human Cognition Reflects Planned Information Processing. - Mark K. Ho; David Abel; Jonathan D. Cohen 003; Michael L. Littman; Thomas L. Griffiths 001 (CoRR, 2020)

Using Machine Teaching to Investigate Human Assumptions when Teaching Reinforcement Learners. - Yun-Shiuan Chuang; Xuezhou Zhang; Yuzhe Ma; Mark K. Ho; Joseph L. Austerweil; Xiaojin Zhu 001 (CoRR, 2020)

Learning Rewards From Linguistic Feedback. - Theodore R. Sumers; Mark K. Ho; Robert X. D. Hawkins; Karthik Narasimhan; Thomas L. Griffiths 001 (AAAI, 2021)

Using Machine Teaching to Investigate Human Assumptions when Teaching Reinforcement Learners. - Yun-Shiuan Chuang; Xuezhou Zhang; Yuzhe Ma; Mark K. Ho; Joseph L. Austerweil; Jerry Zhu (CogSci, 2021)

Extending rational models of communication from beliefs to actions. - Theodore R. Sumers; Robert D. Hawkins; Mark K. Ho; Tom Griffiths 001 (CogSci, 2021)

Specialization and selective social attention establishes the balance between individual and social learning. - Charley M. Wu; Mark K. Ho; Benjamin Kahl; Christina Leuker; Björn Meder; Ralf H. J. M. Kurvers (CogSci, 2021)

On the Expressivity of Markov Reward. - David Abel; Will Dabney; Anna Harutyunyan; Mark K. Ho; Michael L. Littman; Doina Precup; Satinder Singh 001 (NeurIPS, 2021)

Control of mental representations in human planning. - Mark K. Ho; David Abel; Carlos G. Correa; Michael L. Littman; Jonathan D. Cohen 003; Thomas L. Griffiths 001 (CoRR, 2021)

On the Expressivity of Markov Reward (Extended Abstract). - David Abel; Will Dabney; Anna Harutyunyan; Mark K. Ho; Michael L. Littman; Doina Precup; Satinder Singh 001 (IJCAI, 2022)

How to talk so AI will learn: Instructions, descriptions, and autonomy. - Theodore R. Sumers; Robert D. Hawkins; Mark K. Ho; Tom Griffiths 001; Dylan Hadfield-Menell (NeurIPS, 2022)

Cognitive Science as a Source of Forward and Inverse Models of Human Decisions for Robotics and Control. - Mark K. Ho; Thomas L. Griffiths 001 (Annu. Rev. Control. Robotics Auton. Syst., 2022)

Linguistic communication as (inverse) reward design. - Theodore R. Sumers; Robert D. Hawkins; Mark K. Ho; Thomas L. Griffiths 001; Dylan Hadfield-Menell (CoRR, 2022)

How to talk so your robot will learn: Instructions, descriptions, and pragmatics. - Theodore R. Sumers; Robert D. Hawkins; Mark K. Ho; Thomas L. Griffiths 001; Dylan Hadfield-Menell (CoRR, 2022)

On Rate-Distortion Theory in Capacity-Limited Cognition & Reinforcement Learning. - Dilip Arumugam; Mark K. Ho; Noah D. Goodman; Benjamin Van Roy (CoRR, 2022)

Diagnosis, Feedback, Adaptation: A Human-in-the-Loop Framework for Test-Time Policy Adaptation. - Andi Peng; Aviv Netanyahu; Mark K. Ho; Tianmin Shu; Andreea Bobu; Julie Shah; Pulkit Agrawal 001 (ICML, 2023)

Bayesian Reinforcement Learning with Limited Cognitive Load. - Dilip Arumugam; Mark K. Ho; Noah D. Goodman; Benjamin Van Roy (CoRR, 2023)

Structurally guided task decomposition in spatial navigation tasks. - Ruiqi He; Carlos G. Correa; Thomas L. Griffiths 001; Mark K. Ho (CoRR, 2023)

Concept Alignment as a Prerequisite for Value Alignment. - Sunayana Rane; Mark K. Ho; Ilia Sucholutsky; Thomas L. Griffiths 001 (CoRR, 2023)

Exploring the hierarchical structure of human plans via program generation. - Carlos G. Correa; Sophia Sanborn; Mark K. Ho; Frederick Callaway; Nathaniel D. Daw; Thomas L. Griffiths 001 (CoRR, 2023)

Humans decompose tasks by trading off utility and computational cost. - Carlos G. Correa; Mark K. Ho; Frederick Callaway; Nathaniel D. Daw; Thomas L. Griffiths 001 (PLoS Comput. Biol., 2023)

Structurally Guided Task Decomposition in Spatial Navigation Tasks (Student Abstract). - Ruiqi He; Carlos G. Correa; Tom Griffiths 001; Mark K. Ho (AAAI, 2024)

Teaching Functions with Gaussian Process Regression. - Maya Malaviya; Mark K. Ho (AAAI Spring Symposia, 2024)

Three Dogmas of Reinforcement Learning. - David Abel; Mark K. Ho; Anna Harutyunyan (RLJ, 2024)

Representational Alignment Supports Effective Machine Teaching. - 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; et al. (CoRR, 2024)

Building Machines that Learn and Think with People. - Katherine M. Collins; Ilia Sucholutsky; Umang Bhatt; Kartik Chandra; Lionel Wong; Mina Lee 002; Cedegao E. Zhang; Tan Zhi-Xuan; Mark K. Ho; Vikash Mansinghka 001; Adrian Weller; Joshua B. Tenenbaum; et al. (CoRR, 2024)