Thomas L. Griffiths

Publication Activity (10 Years)

Years Active: 2000-2023
Publications (10 Years): 102

Top Venues

CoRR

CogSci

Cogn. Sci.

AAAI

Publications

Bhishma Dedhia, Michael Chang, Jake C. Snell, Thomas L. Griffiths, Niraj K. Jha
Im-Promptu: In-Context Composition from Image Prompts. CoRR (2023)
Takateru Yamakoshi, Robert D. Hawkins, Thomas L. Griffiths
Probing BERT's priors with serial reproduction chains. CoRR (2022)
Maya Malaviya, Ilia Sucholutsky, Kerem Oktar, Thomas L. Griffiths
Can Humans Do Less-Than-One-Shot Learning? CoRR (2022)
Raja Marjieh, Ilia Sucholutsky, Theodore R. Sumers, Nori Jacoby, Thomas L. Griffiths
Predicting Human Similarity Judgments Using Large Language Models. CoRR (2022)
Ishita Dasgupta, Erin Grant, Thomas L. Griffiths
Distinguishing rule- and exemplar-based generalization in learning systems. CoRR (2021)
Shikhar Tuli, Ishita Dasgupta, Erin Grant, Thomas L. Griffiths
Are Convolutional Neural Networks or Transformers more like human vision? CoRR (2021)
Stephan C. Meylan, Thomas L. Griffiths
The Challenges of Large-Scale, Web-Based Language Datasets: Word Length and Predictability Revisited. Cogn. Sci. 45 (6) (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)
Robert X. D. Hawkins, Michael Franke, Michael C. Frank, Kenny Smith, Thomas L. Griffiths, Noah D. Goodman
From partners to populations: A hierarchical Bayesian account of coordination and convention. CoRR (2021)
Theodore R. Sumers, Mark K. Ho, Robert X. D. Hawkins, Karthik Narasimhan, Thomas L. Griffiths
Learning Rewards From Linguistic Feedback. AAAI (2021)
Sonia K. Murthy, Robert X. D. Hawkins, Thomas L. Griffiths
Shades of confusion: Lexical uncertainty modulates ad hoc coordination in an interactive communication task. CoRR (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)
Thomas A. Langlois, H. Charles Zhao, Erin Grant, Ishita Dasgupta, Thomas L. Griffiths, Nori Jacoby
Passive attention in artificial neural networks predicts human visual selectivity. 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)
Michael Chang, Sidhant Kaushik, Sergey Levine, Thomas L. Griffiths
Modularity in Reinforcement Learning via Algorithmic Independence in Credit Assignment. CoRR (2021)
Stephan C. Meylan, Sathvik Nair, Thomas L. Griffiths
Evaluating Models of Robust Word Recognition with Serial Reproduction. CoRR (2021)
Samuel A. Barnett, Robert D. Hawkins, Thomas L. Griffiths
A pragmatic account of the weak evidence effect. CoRR (2021)
Sreejan Kumar, Ishita Dasgupta, Jonathan D. Cohen, Nathaniel D. Daw, Thomas L. Griffiths
Meta-Learning of Structured Task Distributions in Humans and Machines. ICLR (2021)
Frederick Callaway, Antonio Rangel, Thomas L. Griffiths
Fixation patterns in simple choice reflect optimal information sampling. PLoS Comput. Biol. 17 (3) (2021)
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)
Ruairidh M. Battleday, Thomas L. Griffiths
Analogy as Nonparametric Bayesian Inference over Relational Systems. CoRR (2020)
Robert X. D. Hawkins, Takateru Yamakoshi, Thomas L. Griffiths, Adele E. Goldberg
Investigating representations of verb bias in neural language models. EMNLP (1) (2020)
Anna N. Rafferty, Rachel Jansen, Thomas L. Griffiths
Assessing Mathematics Misunderstandings via Bayesian Inverse Planning. Cogn. Sci. 44 (10) (2020)
Rachit Dubey, Erin Grant, Michael Luo, Karthik Narasimhan, Thomas L. Griffiths
Context-Conditioning as Cognitive Control: Guiding Meta-learning with Task Information. CoRR (2020)
Sreejan Kumar, Ishita Dasgupta, Jonathan D. Cohen, Nathaniel D. Daw, Thomas L. Griffiths
Meta-Learning of Compositional Task Distributions in Humans and Machines. CoRR (2020)
Aida Nematzadeh, Zahra Shekarchi, Thomas L. Griffiths, Suzanne Stevenson
Competition in Cross-situational Word Learning: A Computational Study. CoRR (2020)
Vael Gates, Thomas L. Griffiths, Anca D. Dragan
How to Be Helpful to Multiple People at Once. Cogn. Sci. 44 (6) (2020)
R. Thomas McCoy, Erin Grant, Paul Smolensky, Thomas L. Griffiths, Tal Linzen
Universal linguistic inductive biases via meta-learning. CoRR (2020)
Theodore R. Sumers, Mark K. Ho, Robert X. D. Hawkins, Karthik Narasimhan, Thomas L. Griffiths
Learning Rewards from Linguistic Feedback. CoRR (2020)
Robert X. D. Hawkins, Takateru Yamakoshi, Thomas L. Griffiths, Adele E. Goldberg
Investigating representations of verb bias in neural language models. CoRR (2020)
Carlos G. Correa, Mark K. Ho, Fred Callaway, Thomas L. Griffiths
Resource-rational Task Decomposition to Minimize Planning Costs. CoRR (2020)
Robert X. D. Hawkins, Noah D. Goodman, Adele E. Goldberg, Thomas L. Griffiths
Generalizing meanings from partners to populations: Hierarchical inference supports convention formation on networks. CoRR (2020)
Noga Alon, Jonathan D. Cohen, Thomas L. Griffiths, Pasin Manurangsi, Daniel Reichman, Igor Shinkar, Tal Wagner, Alexander Y. Ku
Multitasking Capacity: Hardness Results and Improved Constructions. SIAM J. Discret. Math. 34 (1) (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)
Thomas L. Griffiths
Understanding Human Intelligence through Human Limitations. CoRR (2020)
Pulkit Singh, Joshua C. Peterson, Ruairidh M. Battleday, Thomas L. Griffiths
End-to-end Deep Prototype and Exemplar Models for Predicting Human Behavior. CoRR (2020)
Aditi Jha, Joshua Caleb Peterson, Thomas L. Griffiths
Extracting low-dimensional psychological representations from convolutional neural networks. CoRR (2020)
Michael Chang, Sidhant Kaushik, S. Matthew Weinberg, Thomas L. Griffiths, Sergey Levine
Decentralized Reinforcement Learning: Global Decision-Making via Local Economic Transactions. CoRR (2020)
Mayank Agrawal, Joshua C. Peterson, Thomas L. Griffiths
Using Machine Learning to Guide Cognitive Modeling: A Case Study in Moral Reasoning. CoRR (2019)
Joshua C. Peterson, Ruairidh M. Battleday, Thomas L. Griffiths, Olga Russakovsky
Human Uncertainty Makes Classification More Robust. ICCV (2019)
David D. Bourgin, Joshua C. Peterson, Daniel Reichman, Stuart J. Russell, Thomas L. Griffiths
Cognitive model priors for predicting human decisions. ICML (2019)
Joseph L. Austerweil, Sophia Sanborn, Thomas L. Griffiths
Learning How to Generalize. Cogn. Sci. 43 (8) (2019)
Mark K. Ho, Joanna Korman, Thomas L. Griffiths
The Computational Structure of Unintentional Meaning. CoRR (2019)
Ori Plonsky, Reut Apel, Eyal Ert, Moshe Tennenholtz, David Bourgin, Joshua C. Peterson, Daniel Reichman, Thomas L. Griffiths, Stuart J. Russell, Evan C. Carter, James F. Cavanagh, Ido Erev
Predicting human decisions with behavioral theories and machine learning. CoRR (2019)
Ruairidh M. Battleday, Joshua C. Peterson, Thomas L. Griffiths
Capturing human categorization of natural images at scale by combining deep networks and cognitive models. CoRR (2019)
Mayank Agrawal, Joshua C. Peterson, Thomas L. Griffiths
Scaling up Psychology via Scientific Regret Minimization: A Case Study in Moral Decision-Making. CoRR (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)
Michael Chang, Abhishek Gupta, Sergey Levine, Thomas L. Griffiths
Automatically Composing Representation Transformations as a Means for Generalization. ICLR (Poster) (2019)
David D. Bourgin, Joshua C. Peterson, Daniel Reichman, Thomas L. Griffiths, Stuart J. Russell
Cognitive Model Priors for Predicting Human Decisions. CoRR (2019)
Joshua C. Peterson, Ruairidh M. Battleday, Thomas L. Griffiths, Olga Russakovsky
Human uncertainty makes classification more robust. CoRR (2019)
Kaylee Burns, Aida Nematzadeh, Erin Grant, Alison Gopnik, Thomas L. Griffiths
Exploiting Attention to Reveal Shortcomings in Memory Models. BlackboxNLP@EMNLP (2018)
Noga Alon, Jonathan D. Cohen, Thomas L. Griffiths, Pasin Manurangsi, Daniel Reichman, Igor Shinkar, Tal Wagner, Alexander Y. Ku
Multitasking Capacity: Hardness Results and Improved Constructions. CoRR (2018)
Erin Grant, Chelsea Finn, Sergey Levine, Trevor Darrell, Thomas L. Griffiths
Recasting Gradient-Based Meta-Learning as Hierarchical Bayes. ICLR (Poster) (2018)
Joshua C. Peterson, Jordan W. Suchow, Krisha Aghi, Alexander Y. Ku, Thomas L. Griffiths
Capturing human category representations by sampling in deep feature spaces. CoRR (2018)
Rachit Dubey, Pulkit Agrawal, Deepak Pathak, Thomas L. Griffiths, Alexei A. Efros
Investigating Human Priors for Playing Video Games. CoRR (2018)
Joshua C. Peterson, Paul Soulos, Aida Nematzadeh, Thomas L. Griffiths
Learning Hierarchical Visual Representations in Deep Neural Networks Using Hierarchical Linguistic Labels. CoRR (2018)
Andrew Whalen, Thomas L. Griffiths, Daphna Buchsbaum
Sensitivity to Shared Information in Social Learning. Cogn. Sci. 42 (1) (2018)
Max Simchowitz, Kevin G. Jamieson, Jordan W. Suchow, Thomas L. Griffiths
Adaptive Sampling for Convex Regression. CoRR (2018)
Falk Lieder, Amitai Shenhav, Sebastian Musslick, Thomas L. Griffiths
Rational metareasoning and the plasticity of cognitive control. PLoS Comput. Biol. 14 (4) (2018)
Erin Grant, Chelsea Finn, Sergey Levine, Trevor Darrell, Thomas L. Griffiths
Recasting Gradient-Based Meta-Learning as Hierarchical Bayes. CoRR (2018)
Jordan W. Suchow, Joshua C. Peterson, Thomas L. Griffiths
Learning a face space for experiments on human identity. CoRR (2018)
Ghassen Jerfel, Erin Grant, Thomas L. Griffiths, Katherine A. Heller
Online gradient-based mixtures for transfer modulation in meta-learning. CoRR (2018)
Michael Chang, Abhishek Gupta, Sergey Levine, Thomas L. Griffiths
Automatically Composing Representation Transformations as a Means for Generalization. CoRR (2018)
Pengfei Liu, Ji Zhang, Cane Wing-Ki Leung, Chao He, Thomas L. Griffiths
Exploiting Effective Representations for Chinese Sentiment Analysis Using a Multi-Channel Convolutional Neural Network. CoRR (2018)
Rachit Dubey, Pulkit Agrawal, Deepak Pathak, Alyosha A. Efros, Thomas L. Griffiths
Investigating Human Priors for Playing Video Games. ICLR (Workshop) (2018)
Aida Nematzadeh, Kaylee Burns, Erin Grant, Alison Gopnik, Thomas L. Griffiths
Evaluating Theory of Mind in Question Answering. CoRR (2018)
Chang Liu, Jessica B. Hamrick, Jaime F. Fisac, Anca D. Dragan, J. Karl Hedrick, S. Shankar Sastry, Thomas L. Griffiths
Goal Inference Improves Objective and Perceived Performance in Human-Robot Collaboration. CoRR (2018)
Sophia Sanborn, David D. Bourgin, Michael Chang, Thomas L. Griffiths
Representational efficiency outweighs action efficiency in human program induction. CoRR (2018)
Joshua C. Peterson, Joshua T. Abbott, Thomas L. Griffiths
Evaluating (and Improving) the Correspondence Between Deep Neural Networks and Human Representations. Cogn. Sci. 42 (8) (2018)
Jaime F. Fisac, Chang Liu, Jessica B. Hamrick, S. Shankar Sastry, J. Karl Hedrick, Thomas L. Griffiths, Anca D. Dragan
Generating Plans that Predict Themselves. CoRR (2018)
Frederick Callaway, Sayan Gul, Paul M. Krueger, Thomas L. Griffiths, Falk Lieder
Learning to select computations. UAI (2018)
Aida Nematzadeh, Kaylee Burns, Erin Grant, Alison Gopnik, Thomas L. Griffiths
Evaluating Theory of Mind in Question Answering. EMNLP (2018)
Joshua C. Peterson, Joshua T. Abbott, Thomas L. Griffiths
Leveraging deep neural networks to capture psychological representations. CoRR (2017)
Aida Nematzadeh, Stephan C. Meylan, Thomas L. Griffiths
Evaluating Vector-Space Models of Word Representation, or, The Unreasonable Effectiveness of Counting Words Near Other Words. CogSci (2017)
Smitha Milli, Falk Lieder, Thomas L. Griffiths
When Does Bounded-Optimal Metareasoning Favor Few Cognitive Systems? AAAI (2017)
Jaime F. Fisac, Monica A. Gates, Jessica B. Hamrick, Chang Liu, Dylan Hadfield-Menell, Malayandi Palaniappan, Dhruv Malik, S. Shankar Sastry, Thomas L. Griffiths, Anca D. Dragan
Pragmatic-Pedagogic Value Alignment. ISRR (2017)
Rachit Dubey, Thomas L. Griffiths
A rational analysis of curiosity. CoRR (2017)
Jaime F. Fisac, Monica A. Gates, Jessica B. Hamrick, Chang Liu, Dylan Hadfield-Menell, Malayandi Palaniappan, Dhruv Malik, S. Shankar Sastry, Thomas L. Griffiths, Anca D. Dragan
Pragmatic-Pedagogic Value Alignment. CoRR (2017)
Stephan C. Meylan, Thomas L. Griffiths
Word forms - not just their lengths- are optimized for efficient communication. CoRR (2017)
Falk Lieder, Frederick Callaway, Sayan Gul, Paul M. Krueger, Thomas L. Griffiths
Learning to select computations. CoRR (2017)
Joshua C. Peterson, Joshua T. Abbott, Thomas L. Griffiths
Adapting Deep Network Features to Capture Psychological Representations: An Abridged Report. IJCAI (2017)
Monica A. Gates, Jordan W. Suchow, Thomas L. Griffiths
Empirical tests of large-scale collaborative recall. CogSci (2017)
Thomas Langlois, Nori Jacoby, Jordan W. Suchow, Thomas L. Griffiths
Uncovering visual priors in spatial memory using serial reproduction. CogSci (2017)
Joshua C. Peterson, Thomas L. Griffiths
Evidence for the size principle in semantic and perceptual domains. CoRR (2017)
Joshua C. Peterson, Thomas L. Griffiths
Evidence for the size principle in semantic and perceptual domains. CogSci (2017)
Dawn Chen, Joshua C. Peterson, Thomas L. Griffiths
Evaluating vector-space models of analogy. CoRR (2017)
Ruairidh M. Battleday, Joshua C. Peterson, Thomas L. Griffiths
Modeling Human Categorization of Natural Images Using Deep Feature Representations. CoRR (2017)
Erin Grant, Aida Nematzadeh, Thomas L. Griffiths
How Can Memory-Augmented Neural Networks Pass a False-Belief Task? CogSci (2017)
Chang Liu, Jessica B. Hamrick, Jaime F. Fisac, Anca D. Dragan, J. Karl Hedrick, S. Shankar Sastry, Thomas L. Griffiths
Goal Inference Improves Objective and Perceived Performance in Human-Robot Collaboration. AAMAS (2016)
Joshua C. Peterson, Joshua T. Abbott, Thomas L. Griffiths
Adapting Deep Network Features to Capture Psychological Representations. CoRR (2016)
Joshua C. Peterson, Joshua T. Abbott, Thomas L. Griffiths
Adapting Deep Network Features to Capture Psychological Representations. CogSci (2016)
Jordan W. Suchow, Thomas J. H. Morgan, Jessica B. Hamrick, Michael D. Pacer, Stephan C. Meylan, Thomas L. Griffiths
Wallace: Automating Cultural Evolution Experiments Through Crowdsourcing. CogSci (2016)
Emily Cibelli, Yang Xu, Joseph L. Austerweil, Thomas L. Griffiths, Terry Regier
The Sapir-Whorf Hypothesis and Probabilistic Inference: Evidence from the Domain of Color. CogSci (2016)
Jordan W. Suchow, Michael D. Pacer, Thomas L. Griffiths
Design from Zeroth Principles. CogSci (2016)
Anna N. Rafferty, Rachel Jansen, Thomas L. Griffiths
Using Inverse Planning for Personalized Feedback. EDM (2016)
Alexander G. Huth, Wendy A. de Heer, Thomas L. Griffiths, Frédéric E. Theunissen, Jack L. Gallant
Natural speech reveals the semantic maps that tile human cerebral cortex. Nat. 532 (7600) (2016)
Neil R. Bramley, Peter Dayan, Thomas L. Griffiths, David A. Lagnado
Formalizing Neurath's Ship: Approximate Algorithms for Online Causal Learning. CoRR (2016)
Anna N. Rafferty, Emma Brunskill, Thomas L. Griffiths, Patrick Shafto
Faster Teaching via POMDP Planning. Cogn. Sci. 40 (6) (2016)
Jaime F. Fisac, Chang Liu, Jessica B. Hamrick, Shankar Sastry, J. Karl Hedrick, Thomas L. Griffiths, Anca D. Dragan
Generating Plans that Predict Themselves. WAFR (2016)

Thomas L. Griffiths.css-eqlu8i{font-size:small;padding-left:4px;}

Publication Activity (10 Years)

Top Topics

Top Venues

Thomas L. Griffiths