default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDThomas L. Griffiths 0001
Tom Griffiths 0001
Person information
- affiliation: Princeton University, Department of Psychology, NJ, USA
- affiliation: University of California, Berkeley, Department of Psychology, USA
Other persons with the same name
- Tom Griffiths 0002 — University of Edinburgh, UK
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
Journal Articles
- 2024
[j46]Stephan C. Meylan, Thomas L. Griffiths:
Word Forms Reflect Trade-Offs Between Speaker Effort and Robust Listener Recognition. Cogn. Sci. 48(7) (2024)- [j45]Noga Alon, Jonathan D. Cohen, Thomas L. Griffiths, Pasin Manurangsi, Daniel Reichman, Igor Shinkar, Tal Wagner:
Erratum: Multitasking Capacity: Hardness Results and Improved Constructions. SIAM J. Discret. Math. 38(2): 2001-2003 (2024) - [j44]Theodore R. Sumers, Shunyu Yao, Karthik Narasimhan, Thomas L. Griffiths:
Cognitive Architectures for Language Agents. Trans. Mach. Learn. Res. 2024 (2024) - 2023
- [j43]Aditi Jha, Joshua C. Peterson, Thomas L. Griffiths:
Extracting Low-Dimensional Psychological Representations from Convolutional Neural Networks. Cogn. Sci. 47(1) (2023) - [j42]Natalia Vélez, Brian R. Christian, Mathew D. Hardy, Bill D. Thompson, Thomas L. Griffiths:
How do Humans Overcome Individual Computational Limitations by Working Together? Cogn. Sci. 47(1) (2023) - [j41]Michael Y. Li, Fred Callaway, William D. Thompson, Ryan P. Adams, Thomas L. Griffiths:
Learning to Learn Functions. Cogn. Sci. 47(4) (2023) - [j40]Daniel Reichman, Falk Lieder, David D. Bourgin, Nimrod Talmon, Thomas L. Griffiths:
The Computational Challenges of Means Selection Problems: Network Structure of Goal Systems Predicts Human Performance. Cogn. Sci. 47(8) (2023) - [j39]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) - [j38]Sreejan Kumar, Ishita Dasgupta, Nathaniel D. Daw, Jonathan D. Cohen, Thomas L. Griffiths:
Disentangling Abstraction from Statistical Pattern Matching in Human and Machine Learning. PLoS Comput. Biol. 19(8) (2023) - 2022
- [j37]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: 33-53 (2022) - [j36]Rachit Dubey, Thomas L. Griffiths, Peter Dayan:
The pursuit of happiness: A reinforcement learning perspective on habituation and comparisons. PLoS Comput. Biol. 18(8) (2022) - [j35]Mathew D. Hardy, Peaks M. Krafft, Bill Thompson, Thomas L. Griffiths:
Overcoming Individual Limitations Through Distributed Computation: Rational Information Accumulation in Multigenerational Populations. Top. Cogn. Sci. 14(3): 550-573 (2022) - 2021
- [j34]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) - [j33]Frederick Callaway, Antonio Rangel, Thomas L. Griffiths:
Fixation patterns in simple choice reflect optimal information sampling. PLoS Comput. Biol. 17(3) (2021) - 2020
- [j32]Vael Gates, Thomas L. Griffiths, Anca D. Dragan:
How to Be Helpful to Multiple People at Once. Cogn. Sci. 44(6) (2020) - [j31]Anna N. Rafferty, Rachel Jansen, Thomas L. Griffiths:
Assessing Mathematics Misunderstandings via Bayesian Inverse Planning. Cogn. Sci. 44(10) (2020) - [j30]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): 885-903 (2020) - 2019
- [j29]Joseph L. Austerweil, Sophia Sanborn, Thomas L. Griffiths:
Learning How to Generalize. Cogn. Sci. 43(8) (2019) - 2018
- [j28]Andrew Whalen, Thomas L. Griffiths, Daphna Buchsbaum:
Sensitivity to Shared Information in Social Learning. Cogn. Sci. 42(1): 168-187 (2018) - [j27]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): 2648-2669 (2018) - [j26]Falk Lieder, Amitai Shenhav, Sebastian Musslick, Thomas L. Griffiths:
Rational metareasoning and the plasticity of cognitive control. PLoS Comput. Biol. 14(4) (2018) - 2016
- [j25]Anna N. Rafferty, Emma Brunskill, Thomas L. Griffiths, Patrick Shafto:
Faster Teaching via POMDP Planning. Cogn. Sci. 40(6): 1290-1332 (2016) - [j24]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): 453-458 (2016) - [j23]Thomas L. Griffiths, Joshua T. Abbott, Anne S. Hsu:
Exploring Human Cognition Using Large Image Databases. Top. Cogn. Sci. 8(3): 569-588 (2016) - 2015
- [j22]Anna N. Rafferty, Michelle M. LaMar, Thomas L. Griffiths:
Inferring Learners' Knowledge From Their Actions. Cogn. Sci. 39(3): 584-618 (2015) - [j21]Thomas L. Griffiths, Falk Lieder, Noah D. Goodman:
Rational Use of Cognitive Resources: Levels of Analysis Between the Computational and the Algorithmic. Top. Cogn. Sci. 7(2): 217-229 (2015) - 2014
- [j20]Edward Vul, Noah D. Goodman, Thomas L. Griffiths, Joshua B. Tenenbaum:
One and Done? Optimal Decisions From Very Few Samples. Cogn. Sci. 38(4): 599-637 (2014) - [j19]Anna N. Rafferty, Thomas L. Griffiths, Dan Klein:
Analyzing the Rate at Which Languages Lose the Influence of a Common Ancestor. Cogn. Sci. 38(7): 1406-1431 (2014) - [j18]Luke Maurits, Thomas L. Griffiths:
Tracing the roots of syntax with Bayesian phylogenetics. Proc. Natl. Acad. Sci. USA 111(37): 13576-13581 (2014) - 2013
- [j17]Thomas L. Griffiths, Stephan Lewandowsky, Michael L. Kalish:
The Effects of Cultural Transmission Are Modulated by the Amount of Information Transmitted. Cogn. Sci. 37(5): 953-967 (2013) - [j16]Alexandre Bouchard-Côté, David Hall, Thomas L. Griffiths, Dan Klein:
Automated reconstruction of ancient languages using probabilistic models of sound change. Proc. Natl. Acad. Sci. USA 110(11): 4224-4229 (2013) - 2012
- [j15]Jay B. Martin, Thomas L. Griffiths, Adam Sanborn:
Testing the Efficiency of Markov Chain Monte Carlo With People Using Facial Affect Categories. Cogn. Sci. 36(1): 150-162 (2012) - 2011
- [j14]Joseph L. Austerweil, Thomas L. Griffiths:
Seeking Confirmation Is Rational for Deterministic Hypotheses. Cogn. Sci. 35(3): 499-526 (2011) - [j13]Thomas L. Griffiths, David M. Sobel, Joshua B. Tenenbaum, Alison Gopnik:
Bayes and Blickets: Effects of Knowledge on Causal Induction in Children and Adults. Cogn. Sci. 35(8): 1407-1455 (2011) - [j12]Thomas L. Griffiths, Zoubin Ghahramani:
The Indian Buffet Process: An Introduction and Review. J. Mach. Learn. Res. 12: 1185-1224 (2011) - [j11]Sharon Goldwater, Thomas L. Griffiths, Mark Johnson:
Producing Power-Law Distributions and Damping Word Frequencies with Two-Stage Language Models. J. Mach. Learn. Res. 12: 2335-2382 (2011) - 2010
- [j10]Christopher G. Lucas, Thomas L. Griffiths:
Learning the Form of Causal Relationships Using Hierarchical Bayesian Models. Cogn. Sci. 34(1): 113-147 (2010) - [j9]David M. Blei, Thomas L. Griffiths, Michael I. Jordan:
The nested chinese restaurant process and bayesian nonparametric inference of topic hierarchies. J. ACM 57(2): 7:1-7:30 (2010) - [j8]Michal Rosen-Zvi, Chaitanya Chemudugunta, Thomas L. Griffiths, Padhraic Smyth, Mark Steyvers:
Learning author-topic models from text corpora. ACM Trans. Inf. Syst. 28(1): 4:1-4:38 (2010) - 2009
- [j7]Stephan Lewandowsky, Thomas L. Griffiths, Michael L. Kalish:
The Wisdom of Individuals: Exploring People's Knowledge About Everyday Events Using Iterated Learning. Cogn. Sci. 33(6): 969-998 (2009) - 2008
- [j6]Thomas L. Griffiths, Brian R. Christian, Michael L. Kalish:
Using Category Structures to Test Iterated Learning as a Method for Identifying Inductive Biases. Cogn. Sci. 32(1): 68-107 (2008) - [j5]Noah D. Goodman, Joshua B. Tenenbaum, Jacob Feldman, Thomas L. Griffiths:
A Rational Analysis of Rule-Based Concept Learning. Cogn. Sci. 32(1): 108-154 (2008) - [j4]Daniel J. Navarro, Thomas L. Griffiths:
Latent Features in Similarity Judgments: A Nonparametric Bayesian Approach. Neural Comput. 20(11): 2597-2628 (2008) - [j3]Mike Dowman, Virginia Savova, Thomas L. Griffiths, Konrad P. Körding, Joshua B. Tenenbaum, Matthew Purver:
A Probabilistic Model of Meetings That Combines Words and Discourse Features. IEEE Trans. Speech Audio Process. 16(7): 1238-1248 (2008) - 2007
- [j2]Thomas L. Griffiths, Michael L. Kalish:
Language Evolution by Iterated Learning With Bayesian Agents. Cogn. Sci. 31(3): 441-480 (2007) - [j1]Tomoharu Iwata, Kazumi Saito, Naonori Ueda, Sean Stromsten, Thomas L. Griffiths, Joshua B. Tenenbaum:
Parametric Embedding for Class Visualization. Neural Comput. 19(9): 2536-2556 (2007)
Conference and Workshop Papers
- 2024
- [c223]Ruiqi He, Carlos G. Correa, Tom Griffiths, Mark K. Ho:
Structurally Guided Task Decomposition in Spatial Navigation Tasks (Student Abstract). AAAI 2024: 23512-23513 - [c222]Maya Malaviya, Ilia Sucholutsky, Thomas L. Griffiths:
Pushing the Limits of Learning from Limited Data. AAAI Spring Symposia 2024: 559-561 - [c221]Bonan Zhao, Natalia Vélez, Thomas L. Griffiths:
Comparing Human Behavior to an Optimal Policy for Innovation. AAAI Spring Symposia 2024: 598-599 - [c220]Andi Peng, Andreea Bobu, Belinda Z. Li, Theodore R. Sumers, Ilia Sucholutsky, Nishanth Kumar, Thomas L. Griffiths, Julie A. Shah:
Preference-Conditioned Language-Guided Abstraction. HRI 2024: 572-581 - [c219]Gianluca M. Bencomo, Jake Snell, Thomas L. Griffiths:
Implicit Maximum a Posteriori Filtering via Adaptive Optimization. ICLR 2024 - [c218]Andi Peng, Ilia Sucholutsky, Belinda Z. Li, Theodore R. Sumers, Thomas L. Griffiths, Jacob Andreas, Julie Shah:
Learning with Language-Guided State Abstractions. ICLR 2024 - [c217]Ryan Liu, Theodore R. Sumers, Ishita Dasgupta, Thomas L. Griffiths:
How do Large Language Models Navigate Conflicts between Honesty and Helpfulness? ICML 2024 - [c216]Yufei Tian, Abhilasha Ravichander, Lianhui Qin, Ronan Le Bras, Raja Marjieh, Nanyun Peng, Yejin Choi, Thomas L. Griffiths, Faeze Brahman:
MacGyver: Are Large Language Models Creative Problem Solvers? NAACL-HLT 2024: 5303-5324 - 2023
- [c215]Mathew D. Hardy, Ilia Sucholutsky, Bill Thompson, Tom Griffiths:
Large language models meet cognitive science: LLMs as tools, models, and participants. CogSci 2023 - [c214]Raja Marjieh, Ilia Sucholutsky, Pol van Rijn, Nori Jacoby, Tom Griffiths:
What Language Reveals about Perception: Distilling Psychophysical Knowledge from Large Language Models. CogSci 2023 - [c213]Joshua C. Peterson, Marina Mancoridis, Tom Griffiths:
To each their own theory: Exploring the limits of individual differences in decisions under risk. CogSci 2023 - [c212]Sunayana Rane, Mira L. Nencheva, Zeyu Wang, Casey Lew-Williams, Olga Russakovsky, Tom Griffiths:
Predicting Word Learning in Children from the Performance of Computer Vision Systems. CogSci 2023 - [c211]Cameron Rouse Turner, Thomas J. H. Morgan, Tom Griffiths:
The joint evolution of sensory systems and decision policy allows cognition. CogSci 2023 - [c210]Feng Xia, Jian-Qiao Zhu, Tom Griffiths:
Comparing Human Predictions from Expert Advice to On-line Optimization Algorithms. CogSci 2023 - [c209]Jian-Qiao Zhu, Adam Sanborn, Nick Chater, Tom Griffiths:
Computation-Limited Bayesian Updating. CogSci 2023 - [c208]Michael Chang, Alyssa L. Dayan, Franziska Meier, Thomas L. Griffiths, Sergey Levine, Amy Zhang:
Hierarchical Abstraction for Combinatorial Generalization in Object Rearrangement. ICLR 2023 - [c207]Raja Marjieh, Pol van Rijn, Ilia Sucholutsky, Theodore R. Sumers, Harin Lee, Thomas L. Griffiths, Nori Jacoby:
Words are all you need? Language as an approximation for human similarity judgments. ICLR 2023 - [c206]Raja Marjieh, Ilia Sucholutsky, Thomas A. Langlois, Nori Jacoby, Thomas L. Griffiths:
Analyzing Diffusion as Serial Reproduction. ICML 2023: 24005-24019 - [c205]Bhishma Dedhia, Michael Chang, Jake Snell, Tom Griffiths, Niraj K. Jha:
Im-Promptu: In-Context Composition from Image Prompts. NeurIPS 2023 - [c204]Ilia Sucholutsky, Tom Griffiths:
Alignment with human representations supports robust few-shot learning. NeurIPS 2023 - [c203]Zi Wang, Alexander Ku, Jason Baldridge, Tom Griffiths, Been Kim:
Gaussian Process Probes (GPP) for Uncertainty-Aware Probing. NeurIPS 2023 - [c202]Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Tom Griffiths, Yuan Cao, Karthik Narasimhan:
Tree of Thoughts: Deliberate Problem Solving with Large Language Models. NeurIPS 2023 - [c201]Michael Y. Li, Erin Grant, Thomas L. Griffiths:
Gaussian Process Surrogate Models for Neural Networks. UAI 2023: 1241-1252 - [c200]Ilia Sucholutsky, Ruairidh M. Battleday, Katherine M. Collins, Raja Marjieh, Joshua C. Peterson, Pulkit Singh, Umang Bhatt, Nori Jacoby, Adrian Weller, Thomas L. Griffiths:
On the informativeness of supervision signals. UAI 2023: 2036-2046 - 2022
- [c199]Takateru Yamakoshi, Thomas L. Griffiths, Robert D. Hawkins:
Probing BERT's priors with serial reproduction chains. ACL (Findings) 2022: 3977-3992 - [c198]Maya Malaviya, Ilia Sucholutsky, Kerem Oktar, Tom Griffiths:
Can Humans Do Less-Than-One-Shot Learning? CogSci 2022 - [c197]Raja Marjieh, Ilia Sucholutsky, Theodore R. Sumers, Nori Jacoby, Tom Griffiths:
Predicting Human Similarity Judgments Using Large Language Models. CogSci 2022 - [c196]Ishita Dasgupta, Erin Grant, Tom Griffiths:
Distinguishing rule and exemplar-based generalization in learning systems. ICML 2022: 4816-4830 - [c195]Michael Chang, Tom Griffiths, Sergey Levine:
Object Representations as Fixed Points: Training Iterative Refinement Algorithms with Implicit Differentiation. NeurIPS 2022 - [c194]Sreejan Kumar, Carlos G. Correa, Ishita Dasgupta, Raja Marjieh, Michael Y. Hu, Robert D. Hawkins, Jonathan D. Cohen, Nathaniel D. Daw, Karthik Narasimhan, Tom Griffiths:
Using natural language and program abstractions to instill human inductive biases in machines. NeurIPS 2022 - [c193]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 - 2021
- [c192]Theodore R. Sumers, Mark K. Ho, Robert X. D. Hawkins, Karthik Narasimhan, Thomas L. Griffiths:
Learning Rewards From Linguistic Feedback. AAAI 2021: 6002-6010 - [c191]Arjun Devraj, Qiong Zhang, Tom Griffiths:
The Dynamics of Exemplar and Prototype Representations Depend on Environmental Statistics. CogSci 2021 - [c190]Sammy Floyd, Kavindya Dalawella, Adele Goldberg, Casey Lew-Williams, Tom Griffiths:
Modeling rules and similarity in colexification. CogSci 2021 - [c189]Robert D. Hawkins, Irina Liu, Adele Goldberg, Tom Griffiths:
Respect the code: Speakers expect novel conventions to generalize within but not across social group boundaries. CogSci 2021 - [c188]Zahra Shekarchi, Aida Nematzadeh, Tom Griffiths, Suzanne Stevenson:
Mutual Exclusivity as Competition in Cross-situational Word Learning. CogSci 2021 - [c187]Theodore R. Sumers, Robert D. Hawkins, Mark K. Ho, Tom Griffiths:
Extending rational models of communication from beliefs to actions. CogSci 2021 - [c186]Shikhar Tuli, Ishita Dasgupta, Erin Grant, Tom Griffiths:
Are Convolutional Neural Networks or Transformers more like human vision? CogSci 2021 - [c185]Samarie A. Wilson, Somya Arora, Qiong Zhang, Tom Griffiths:
A Rational Account of Anchor Effects in Hindsight Bias. CogSci 2021 - [c184]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 - [c183]Michael Chang, Sidhant Kaushik, Sergey Levine, Tom Griffiths:
Modularity in Reinforcement Learning via Algorithmic Independence in Credit Assignment. ICML 2021: 1452-1462 - [c182]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. NeurIPS 2021: 27094-27106 - 2020
- [c181]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: 1300-1307 - [c180]Ruairidh M. Battleday, Tom Griffiths:
Analogy as Nonparametric Bayesian Inference over Relational Systems. CogSci 2020 - [c179]Frederick Callaway, Mathew D. Hardy, Tom Griffiths:
Optimal nudging. CogSci 2020 - [c178]Carlos G. Correa, Mark K. Ho, Frederick Callaway, Tom Griffiths:
Resource-rational Task Decomposition to Minimize Planning Costs. CogSci 2020 - [c177]Mathew D. Hardy, Bill Thompson, Peter M. Krafft, Tom Griffiths:
Population-level amplification of perceptual bias. CogSci 2020 - [c176]Robert X. D. Hawkins, Noah D. Goodman, Adele E. Goldberg, Tom Griffiths:
Generalizing meanings from partners to populations: Hierarchical inference supports convention formation on networks. CogSci 2020 - [c175]Samee Ibraheem, Vael Gates, John DeNero, Tom Griffiths:
Investigating the Behavior of Malicious Actors Through the Game of Mafia. CogSci 2020 - [c174]Rachel Jansen, Anna N. Rafferty, Tom Griffiths:
A rational model of sequential self-assessment. CogSci 2020 - [c173]Aditi Jha, Joshua C. Peterson, Tom Griffiths:
Extracting low-dimensional psychological representations from convolutional neural networks. CogSci 2020 - [c172]Max Kleiman-Weiner, Felix Sosa, Bill Thompson, Sebastiaan van Opheusden, Tom Griffiths, Samuel Gershman, Fiery Cushman:
Downloading Culture.zip: Social learning by program induction. CogSci 2020 - [c171]Richard Thomas McCoy, Erin Grant, Paul Smolensky, Tom Griffiths, Tal Linzen:
Universal linguistic inductive biases via meta-learning. CogSci 2020 - [c170]Pulkit Singh, Joshua C. Peterson, Ruairidh M. Battleday, Tom Griffiths:
End-to-end Deep Prototype and Exemplar Models for Predicting Human Behavior. CogSci 2020 - [c169]Jordan W. Suchow, Tom Griffiths, Joshua K. Hartshorne:
Workshop on Scaling Cognitive Science. CogSci 2020 - [c168]Theodore R. Sumers, Mark K. Ho, Tom Griffiths:
Show or Tell? Demonstration is More Robust to Changes in Shared Perception than Explanation. CogSci 2020 - [c167]Qiong Zhang, Kenneth A. Norman, Tom Griffiths:
The method of loci is an optimal policy for memory search. CogSci 2020 - [c166]Robert X. D. Hawkins, Takateru Yamakoshi, Thomas L. Griffiths, Adele E. Goldberg:
Investigating representations of verb bias in neural language models. EMNLP (1) 2020: 4653-4663 - [c165]Michael Chang, Sidhant Kaushik, S. Matthew Weinberg, Tom Griffiths, Sergey Levine:
Decentralized Reinforcement Learning: Global Decision-Making via Local Economic Transactions. ICML 2020: 1437-1447 - 2019
- [c164]Emmanuel M. Pothos, Jerome R. Busemeyer, Timothy J. Pleskac, James M. Yearsley, Josh Tenenbaum, Noah D. Goodman, Michael Henry Tessler, Tom Griffiths, Falk Lieder, Ralph Hertwig, Thorsten Pachur, Christina Leuker, Richard M. Shiffrin:
Extending Rationality. CogSci 2019: 39-40 - [c163]Rachit Dubey, Tom Griffiths, Tania Lombrozo:
If it's important, then I am curious: A value intervention to induce curiosity. CogSci 2019: 282-288 - [c162]Bill Thompson, Tom Griffiths:
Inductive Biases Constrain Cumulative Cultural Evolution. CogSci 2019: 1111-1117 - [c161]Mayank Agrawal, Joshua C. Peterson, Tom Griffiths:
Using Machine Learning to Guide Cognitive Modeling: A Case Study in Moral Reasoning. CogSci 2019: 1318-1323 - [c160]Erin Grant, Joshua C. Peterson, Tom Griffiths:
Learning deep taxonomic priors for concept learning from few positive examples. CogSci 2019: 1865-1870 - [c159]Mark K. Ho, Joanna Korman, Tom Griffiths:
The Computational Structure of Unintentional Meaning. CogSci 2019: 1915-1921 - [c158]Thomas Langlois, Nori Jacoby, Jordan W. Suchow, Tom Griffiths:
Orthogonal multi-view three-dimensional object representations in memory revealed by serial reproduction. CogSci 2019: 2078-2083 - [c157]Carlos G. Correa, Frederick Callaway, Mark K. Ho, Tom Griffiths:
Compositional subgoal representations. CogSci 2019: 3255 - [c156]Rachit Dubey, Pulkit Agrawal, Deepak Pathak, Alyosha A. Efros, Tom Griffiths:
Human-level but not human-like: Deep Reinforcement Learning in the dark. CogSci 2019: 3265 - [c155]Mathew D. Hardy, Tom Griffiths:
Demonstrating the Impact of Prior Knowledge in Risky Choice. CogSci 2019: 3278 - [c154]Vishal Lall, Jordan W. Suchow, Gustavo Malkomes, Tom Griffiths:
Automated cognitive modeling with Bayesian active model selection. CogSci 2019: 3503 - [c153]Anna N. Rafferty, Rachel Jansen, Tom Griffiths:
Modeling students' fraction arithmetic strategies using inverse planning. CogSci 2019: 3554 - [c152]Joshua C. Peterson, Ruairidh M. Battleday, Thomas L. Griffiths, Olga Russakovsky:
Human Uncertainty Makes Classification More Robust. ICCV 2019: 9616-9625 - [c151]Michael Chang, Abhishek Gupta, Sergey Levine, Thomas L. Griffiths:
Automatically Composing Representation Transformations as a Means for Generalization. ICLR (Poster) 2019 - [c150]David D. Bourgin, Joshua C. Peterson, Daniel Reichman, Stuart J. Russell, Thomas L. Griffiths:
Cognitive model priors for predicting human decisions. ICML 2019: 5133-5141 - [c149]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: 5175-5186 - [c148]Ghassen Jerfel, Erin Grant, Tom Griffiths, Katherine A. Heller:
Reconciling meta-learning and continual learning with online mixtures of tasks. NeurIPS 2019: 9119-9130 - 2018
- [c147]David Bourgin, Joshua T. Abbott, Tom Griffiths:
Recommendation as Generalization: Evaluating Cognitive Models In the Wild. CogSci 2018 - [c146]Frederick Callaway, Falk Lieder, Priyam Das, Sayan Gul, Paul M. Krueger, Tom Griffiths:
A resource-rational analysis of human planning. CogSci 2018 - [c145]Rachel Jansen, Ruthe Foushee, Tom Griffiths:
A new similarity measure to reveal individual differences and growth in implicit number conceptions. CogSci 2018 - [c144]Rachel Jansen, Anna N. Rafferty, Tom Griffiths:
Modeling the Dunning-Kruger Effect: A Rational Account of Inaccurate Self-Assessment. CogSci 2018 - [c143]Peter M. Krafft, Tom Griffiths:
Levels of Analysis in Computational Social Science. CogSci 2018 - [c142]Paul M. Krueger, Tom Griffiths:
Shaping Model-Free Habits with Model-Based Goals. CogSci 2018 - [c141]Alexandra Paxton, Thomas J. H. Morgan, Jordan W. Suchow, Tom Griffiths:
Interpersonal Coordination of Perception and Memory in Real-Time Online Social Interaction. CogSci 2018 - [c140]Joshua C. Peterson, Jordan W. Suchow, Krisha Aghi, Alexander Y. Ku, Tom Griffiths:
Capturing human category representations by sampling in deep feature spaces. CogSci 2018 - [c139]Joshua C. Peterson, Paul Soulos, Aida Nematzadeh, Tom Griffiths:
Learning Hierarchical Visual Representations in Deep Neural Networks Using Hierarchical Linguistic Labels. CogSci 2018 - [c138]Sophia Sanborn, David Bourgin, Michael Chang, Tom Griffiths:
Representational efficiency outweighs action efficiency in human program induction. CogSci 2018 - [c137]Jordan W. Suchow, Joshua C. Peterson, Tom Griffiths:
Learning a face space for experiments on human identity. CogSci 2018 - [c136]Kaylee Burns, Aida Nematzadeh, Erin Grant, Alison Gopnik, Thomas L. Griffiths:
Exploiting Attention to Reveal Shortcomings in Memory Models. BlackboxNLP@EMNLP 2018: 378-380 - [c135]Aida Nematzadeh, Kaylee Burns, Erin Grant, Alison Gopnik, Thomas L. Griffiths:
Evaluating Theory of Mind in Question Answering. EMNLP 2018: 2392-2400 - [c134]Rachit Dubey, Pulkit Agrawal, Deepak Pathak, Alyosha A. Efros, Thomas L. Griffiths:
Investigating Human Priors for Playing Video Games. ICLR (Workshop) 2018 - [c133]Erin Grant, Chelsea Finn, Sergey Levine, Trevor Darrell, Thomas L. Griffiths:
Recasting Gradient-Based Meta-Learning as Hierarchical Bayes. ICLR (Poster) 2018 - [c132]Joshua C. Peterson, Krisha Aghi, Jordan W. Suchow, Alexander Y. Ku, Tom Griffiths:
Capturing Human Category Representations by Sampling in Deep Feature Spaces. ICLR (Workshop) 2018 - [c131]Rachit Dubey, Pulkit Agrawal, Deepak Pathak, Tom Griffiths, Alexei A. Efros:
Investigating Human Priors for Playing Video Games. ICML 2018: 1348-1356 - [c130]Frederick Callaway, Sayan Gul, Paul M. Krueger, Thomas L. Griffiths, Falk Lieder:
Learning to select computations. UAI 2018: 776-785 - 2017
- [c129]Smitha Milli, Falk Lieder, Thomas L. Griffiths:
When Does Bounded-Optimal Metareasoning Favor Few Cognitive Systems? AAAI 2017: 4422-4428 - [c128]Ruairidh M. Battleday, Joshua C. Peterson, Tom Griffiths:
Modeling human categorization of natural images using deep feature representations. CogSci 2017 - [c127]David Bourgin, Falk Lieder, Daniel Reichman, Nimrod Talmon, Tom Griffiths:
The Structure of Goal Systems Predicts Human Performance. CogSci 2017 - [c126]Frederick Callaway, Jessica B. Hamrick, Tom Griffiths:
Discovering simple heuristics from mental simulation. CogSci 2017 - [c125]Dawn Chen, Joshua C. Peterson, Tom Griffiths:
Evaluating vector-space models of analogy. CogSci 2017 - [c124]Rachit Dubey, Tom Griffiths:
A rational analysis of curiosity. CogSci 2017 - [c123]Monica A. Gates, Jordan W. Suchow, Thomas L. Griffiths:
Empirical tests of large-scale collaborative recall. CogSci 2017 - [c122]Erin Grant, Aida Nematzadeh, Thomas L. Griffiths:
How Can Memory-Augmented Neural Networks Pass a False-Belief Task? CogSci 2017 - [c121]Jessica B. Hamrick, David Bourgin, Thomas Langlois, Tom Griffiths:
Exploring inductive bias of visual scenes. CogSci 2017 - [c120]Rachel Jansen, Anna N. Rafferty, Tom Griffiths:
Algebra is not like trivia: Evaluating self-assessment in an online math tutor. CogSci 2017 - [c119]Paul M. Krueger, Falk Lieder, Tom Griffiths:
Enhancing metacognitive reinforcement learning using reward structures and feedback. CogSci 2017 - [c118]Thomas Langlois, Nori Jacoby, Jordan W. Suchow, Thomas L. Griffiths:
Uncovering visual priors in spatial memory using serial reproduction. CogSci 2017 - [c117]Falk Lieder, Paul M. Krueger, Tom Griffiths:
An automatic method for discovering rational heuristics for risky choice. CogSci 2017 - [c116]Yuan Meng, Tom Griffiths, Fei Xu:
Inferring Intentional Agents From Violation of Randomness. CogSci 2017 - [c115]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 - [c114]Joshua C. Peterson, Thomas L. Griffiths:
Evidence for the size principle in semantic and perceptual domains. CogSci 2017 - [c113]Joshua C. Peterson, Joshua T. Abbott, Thomas L. Griffiths:
Adapting Deep Network Features to Capture Psychological Representations: An Abridged Report. IJCAI 2017: 4934-4938 - [c112]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: 49-57 - [c111]Noga Alon, Daniel Reichman, Igor Shinkar, Tal Wagner, Sebastian Musslick, Jonathan D. Cohen, Tom Griffiths, Biswadip Dey, Kayhan Özcimder:
A graph-theoretic approach to multitasking. NIPS 2017: 2100-2109 - 2016
- [c110]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: 940-948 - [c109]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 - [c108]Ruthe Foushee, Tom Griffiths, Mahesh Srinivasan:
Lexical Complexity of Child-Directed and Overheard Speech: Implications for Learning. CogSci 2016 - [c107]Robert X. D. Hawkins, Noah D. Goodman, Olga Feher, Kenny Smith, Robert L. Goldstone, Tom Griffiths:
The Emergence of Conventions. CogSci 2016 - [c106]Falk Lieder, Tom Griffiths:
Helping people make better decisions using optimal gamification. CogSci 2016 - [c105]Shaun O'Grady, Tom Griffiths, Fei Xu:
Do Simple Probability Judgments Rely on Integer Approximation? CogSci 2016 - [c104]Joshua C. Peterson, Joshua T. Abbott, Thomas L. Griffiths:
Adapting Deep Network Features to Capture Psychological Representations. CogSci 2016 - [c103]Jordan W. Suchow, Tom Griffiths:
Deciding to Remember: Memory Maintenance as a Markov Decision Process. CogSci 2016 - [c102]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 - [c101]Jordan W. Suchow, Michael D. Pacer, Thomas L. Griffiths:
Design from Zeroth Principles. CogSci 2016 - [c100]Anna N. Rafferty, Rachel Jansen, Thomas L. Griffiths:
Using Inverse Planning for Personalized Feedback. EDM 2016: 472-477 - [c99]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: 144-159 - 2015
- [c98]Anna N. Rafferty, Thomas L. Griffiths:
Interpreting Freeform Equation Solving. AIED 2015: 387-397 - [c97]Jessica B. Hamrick, Kevin A. Smith, Thomas L. Griffiths, Ed Vul:
Think again? The amount of mental simulation tracks uncertainty in the outcome. CogSci 2015 - [c96]Jane C. Hu, Andrew Whalen, Daphna Buchsbaum, Thomas L. Griffiths, Fei Xu:
Can children balance the size of a majority with the quality of their information? CogSci 2015 - [c95]Falk Lieder, Thomas L. Griffiths:
When to use which heuristic: A rational solution to the strategy selection problem. CogSci 2015 - [c94]Falk Lieder, Zi Lin Sim, Jane C. Hu, Thomas L. Griffiths, Fei Xu:
Children and adults differ in their strategies for social learning. CogSci 2015 - [c93]Bradley C. Love, Michael Ramscar, Thomas L. Griffiths, Matt Jones:
Generative and Discriminative Models in Cognitive Science. CogSci 2015 - [c92]Stephan C. Meylan, Thomas L. Griffiths:
A Bayesian Framework for Learning Words From Multiword Utterances. CogSci 2015 - [c91]Thomas J. H. Morgan, Thomas L. Griffiths:
What the Baldwin Effect affects. CogSci 2015 - [c90]Michael Pacer, Thomas L. Griffiths:
Upsetting the contingency table: Causal induction over sequences of point events. CogSci 2015 - [c89]Azzurra Ruggeri, Tania Lombrozo, Thomas L. Griffiths, Fei Xu:
Children search for information as efficiently as adults, but seek additional confirmatory evidence. CogSci 2015 - 2014
- [c88]Maxwell A. Bertolero, Thomas L. Griffiths:
Is Holism A Problem For Inductive Inference? A Computational Analysis. CogSci 2014 - [c87]David Bourgin, Joshua T. Abbott, Thomas L. Griffiths, Kevin A. Smith, Ed Vul:
Empirical Evidence for Markov Chain Monte Carlo in Memory Search. CogSci 2014 - [c86]Jessica B. Hamrick, Thomas L. Griffiths:
What to simulate? Inferring the right direction for mental rotation. CogSci 2014 - [c85]Falk Lieder, Ming Hsu, Thomas L. Griffiths:
The high availability of extreme events serves resource-rational decision-making. CogSci 2014 - [c84]Bradley C. Love, Jana Jarecki, Jerome R. Busemeyer, Niels A. Taatgen, Thomas L. Griffiths, Mirjam Jenny:
Moot Point Process Models. CogSci 2014 - [c83]Stephan C. Meylan, Brett Goldstein, Anna N. Rafferty, Thomas L. Griffiths:
The Telephone Game: Exploring Inductive Biases In Naturalistic Language Use. CogSci 2014 - [c82]Rebecca Neumann, Anna N. Rafferty, Thomas L. Griffiths:
A Bounded Rationality Account of Wishful Thinking. CogSci 2014 - [c81]Avi Press, Michael Pacer, Thomas L. Griffiths, Brian R. Christian:
Caching Algorithms and Rational Models of Memory. CogSci 2014 - [c80]Andrew Whalen, Luke Maurits, Michael Pacer, Thomas L. Griffiths:
Cultural evolution with sparse testimony: when does the cultural ratchet slip? CogSci 2014 - [c79]Anna N. Rafferty, Thomas L. Griffiths:
Diagnosing Algebra Understanding via Bayesian Inverse Planning. EDM 2014: 351-352 - [c78]Falk Lieder, Dillon Plunkett, Jessica B. Hamrick, Stuart J. Russell, Nicholas Hay, Thomas L. Griffiths:
Algorithm selection by rational metareasoning as a model of human strategy selection. NIPS 2014: 2870-2878 - 2013
- [c77]Joshua T. Abbott, Jessica B. Hamrick, Thomas L. Griffiths:
Approximating Bayesian inference with a sparse distributed memory system. CogSci 2013 - [c76]Jessica B. Hamrick, Peter W. Battaglia, Thomas L. Griffiths, Joshua B. Tenenbaum:
Inferring mass in complex physical scenes via probabilistic simulation. CogSci 2013 - [c75]Jane C. Hu, Daphna Buchsbaum, Thomas L. Griffiths, Fei Xu:
When does the majority rule? Preschoolers' trust in majority informants varies by task domain. CogSci 2013 - [c74]Andrew Whalen, Daphna Buchsbaum, Thomas L. Griffiths:
How do you know that? Sensitivity to statistical dependency in social learning. CogSci 2013 - [c73]Yangqing Jia, Joshua T. Abbott, Joseph L. Austerweil, Thomas L. Griffiths, Trevor Darrell:
Visual Concept Learning: Combining Machine Vision and Bayesian Generalization on Concept Hierarchies. NIPS 2013: 1842-1850 - [c72]Michael Pacer, Joseph Jay Williams, Xi Chen, Tania Lombrozo, Thomas L. Griffiths:
Evaluating computational models of explanation using human judgments. UAI 2013 - 2012
- [c71]Joshua T. Abbott, Joseph L. Austerweil, Thomas L. Griffiths:
Constructing a hypothesis space from the Web for large-scale Bayesian word learning. CogSci 2012 - [c70]Joshua T. Abbott, Terry Regier, Thomas L. Griffiths:
Predicting focal colors with a rational model of representativeness. CogSci 2012 - [c69]Charles Blundell, Adam Sanborn, Thomas L. Griffiths:
Look-Ahead Monte Carlo with People. CogSci 2012 - [c68]Daphna Buchsbaum, Sophie Bridgers, Andrew Whalen, Elizabeth Seiver, Thomas L. Griffiths, Alison Gopnik:
Do I know that you know what you know? Modeling testimony in causal inference. CogSci 2012 - [c67]Chris Eliasmith, Thomas L. Griffiths, Valerie Gray Hardcastle, Bradley C. Love, William Bechtel, Richard P. Cooper, David Peebles:
Thirty years of Marr's Vision: Levels of Analysis in Cognitive Science. CogSci 2012 - [c66]Thomas L. Griffiths, Joseph L. Austerweil, Vincent G. Berthiaume:
Comparing the inductive biases of simple neural networks and Bayesian models. CogSci 2012 - [c65]Anne S. Hsu, Jay B. Martin, Adam N. Sanborn, Thomas L. Griffiths:
Identifying representations of categories of discrete items using Markov chain Monte Carlo with People. CogSci 2012 - [c64]Daniel R. Little, Stephan Lewandowsky, Thomas L. Griffiths:
A Bayesian Model of Rule Induction in Raven's Progressive Matrices. CogSci 2012 - [c63]Luke Maurits, Thomas L. Griffiths:
Connecting input filtering and selection in language evolution. CogSci 2012 - [c62]Michael Pacer, Thomas L. Griffiths:
Elements of a rational framework for continuous-time causal induction. CogSci 2012 - [c61]Anna N. Rafferty, Matei Zaharia, Thomas L. Griffiths:
Optimally Designing Games for Cognitive Science Research. CogSci 2012 - [c60]Saiwing Yeung, Christopher G. Lucas, Thomas L. Griffiths:
Determining people's expectations about the form of causal relationships. CogSci 2012 - [c59]Anna N. Rafferty, Michelle M. LaMar, Thomas L. Griffiths:
Inferring learners' knowledge from observed actions. EDM 2012: 226-227 - [c58]Falk Lieder, Thomas L. Griffiths, Noah D. Goodman:
"Burn-in, bias, and the rationality of anchoring". NIPS 2012: 2699-2707 - [c57]Joshua T. Abbott, Joseph L. Austerweil, Thomas L. Griffiths:
Human memory search as a random walk in a semantic network. NIPS 2012: 3050-3058 - 2011
- [c56]Kevin Robert Canini, Thomas L. Griffiths:
A Nonparametric Bayesian Model of Multi-Level Category Learning. AAAI 2011: 307-312 - [c55]Anna N. Rafferty, Thomas L. Griffiths, Marc Ettlinger:
Exploring the Relationship Between Learnability and Linguistic Universals. CMCL@ACL 2011: 49-57 - [c54]Anna N. Rafferty, Emma Brunskill, Thomas L. Griffiths, Patrick Shafto:
Faster Teaching by POMDP Planning. AIED 2011: 280-287 - [c53]Joshua T. Abbott, Thomas L. Griffiths:
Exploring the influence of particle filter parameters on order effects in causal learning. CogSci 2011 - [c52]Joseph L. Austerweil, Thomas L. Griffiths, Todd M. Gureckis, Robert L. Goldstone, Kevin Robert Canini, Matt Jones:
Grow your own representations: Computational constructivism. CogSci 2011 - [c51]Elizabeth Bonawitz, Stephanie Denison, Annie Chen, Alison Gopnik, Thomas L. Griffiths:
A Simple Sequential Algorithm for Approximating Bayesian Inference. CogSci 2011 - [c50]Daphna Buchsbaum, Kevin Robert Canini, Thomas L. Griffiths:
Segmenting and Recognizing Human Action using Low-level Video Features. CogSci 2011 - [c49]Kevin Robert Canini, Thomas L. Griffiths, Wolf Vanpaemel, Michael L. Kalish:
Discovering Inductive Biases in Categorization through Iterated Learning. CogSci 2011 - [c48]Jane C. Hu, Christopher G. Lucas, Thomas L. Griffiths, Fei Xu:
Young Toddlers' Understanding of Graded Preferences. CogSci 2011 - [c47]Christopher G. Lucas, Charles Kemp, Thomas L. Griffiths:
From preferences to choices and back again: evidence for human inconsistency and its implications. CogSci 2011 - [c46]Anna Waisman, Christopher G. Lucas, Thomas L. Griffiths, Lucia Jacobs:
A Bayesian model of navigation in squirrels. CogSci 2011 - [c45]Saiwing Yeung, Thomas L. Griffiths:
Estimating human priors on causal strength. CogSci 2011 - [c44]Joseph L. Austerweil, Abram L. Friesen, Thomas L. Griffiths:
An ideal observer model for identifying the reference frame of objects. NIPS 2011: 514-522 - [c43]Joshua T. Abbott, Katherine A. Heller, Zoubin Ghahramani, Thomas L. Griffiths:
Testing a Bayesian Measure of Representativeness Using a Large Image Database. NIPS 2011: 2321-2329 - [c42]Michael Pacer, Thomas L. Griffiths:
A rational model of causal inference with continuous causes. NIPS 2011: 2384-2392 - 2010
- [c41]Kevin Robert Canini, Mikhail M. Shashkov, Thomas L. Griffiths:
Modeling Transfer Learning in Human Categorization with the Hierarchical Dirichlet Process. ICML 2010: 151-158 - [c40]Joseph L. Austerweil, Thomas L. Griffiths:
Learning invariant features using the Transformed Indian Buffet Process. NIPS 2010: 82-90 - 2009
- [c39]Thomas L. Griffiths:
Connecting human and machine learning via probabilistic models of cognition. INTERSPEECH 2009: 9-12 - [c38]Alexandre Bouchard-Côté, Thomas L. Griffiths, Dan Klein:
Improved Reconstruction of Protolanguage Word Forms. HLT-NAACL 2009: 65-73 - [c37]Anne S. Hsu, Thomas L. Griffiths:
Differential Use of Implicit Negative Evidence in Generative and Discriminative Language Learning. NIPS 2009: 754-762 - [c36]Kurt T. Miller, Thomas L. Griffiths, Michael I. Jordan:
Nonparametric Latent Feature Models for Link Prediction. NIPS 2009: 1276-1284 - [c35]Lei Shi, Thomas L. Griffiths:
Neural Implementation of Hierarchical Bayesian Inference by Importance Sampling. NIPS 2009: 1669-1677 - [c34]Kevin Robert Canini, Lei Shi, Thomas L. Griffiths:
Online Inference of Topics with Latent Dirichlet Allocation. AISTATS 2009: 65-72 - 2008
- [c33]Joseph L. Austerweil, Thomas L. Griffiths:
Analyzing human feature learning as nonparametric Bayesian inference. NIPS 2008: 97-104 - [c32]Thomas L. Griffiths, Christopher G. Lucas, Joseph Jay Williams, Michael L. Kalish:
Modeling human function learning with Gaussian processes. NIPS 2008: 553-560 - [c31]Roger Levy, Florencia Reali, Thomas L. Griffiths:
Modeling the effects of memory on human online sentence processing with particle filters. NIPS 2008: 937-944 - [c30]Christopher G. Lucas, Thomas L. Griffiths, Fei Xu, Christine Fawcett:
A rational model of preference learning and choice prediction by children. NIPS 2008: 985-992 - [c29]Jing Xu, Thomas L. Griffiths:
How memory biases affect information transmission: A rational analysis of serial reproduction. NIPS 2008: 1809-1816 - [c28]Kurt T. Miller, Thomas L. Griffiths, Michael I. Jordan:
The Phylogenetic Indian Buffet Process: A Non-Exchangeable Nonparametric Prior for Latent Features. UAI 2008: 403-410 - 2007
- [c27]Sharon Goldwater, Thomas L. Griffiths:
A fully Bayesian approach to unsupervised part-of-speech tagging. ACL 2007 - [c26]Alexandre Bouchard-Côté, Percy Liang, Thomas L. Griffiths, Dan Klein:
A Probabilistic Approach to Diachronic Phonology. EMNLP-CoNLL 2007: 887-896 - [c25]Mark Johnson, Thomas L. Griffiths, Sharon Goldwater:
Bayesian Inference for PCFGs via Markov Chain Monte Carlo. HLT-NAACL 2007: 139-146 - [c24]Alexandre Bouchard-Côté, Percy Liang, Thomas L. Griffiths, Dan Klein:
A Probabilistic Approach to Language Change. NIPS 2007: 169-176 - [c23]Adam Sanborn, Thomas L. Griffiths:
Markov Chain Monte Carlo with People. NIPS 2007: 1265-1272 - 2006
- [c22]Charles Kemp, Joshua B. Tenenbaum, Thomas L. Griffiths, Takeshi Yamada, Naonori Ueda:
Learning Systems of Concepts with an Infinite Relational Model. AAAI 2006: 381-388 - [c21]Sharon Goldwater, Thomas L. Griffiths, Mark Johnson:
Contextual Dependencies in Unsupervised Word Segmentation. ACL 2006 - [c20]Matthew Purver, Konrad P. Körding, Thomas L. Griffiths, Joshua B. Tenenbaum:
Unsupervised Topic Modelling for Multi-Party Spoken Discourse. ACL 2006 - [c19]Mark Johnson, Thomas L. Griffiths, Sharon Goldwater:
Adaptor Grammars: A Framework for Specifying Compositional Nonparametric Bayesian Models. NIPS 2006: 641-648 - [c18]Daniel J. Navarro, Thomas L. Griffiths:
A Nonparametric Bayesian Method for Inferring Features From Similarity Judgments. NIPS 2006: 1033-1040 - [c17]Frank D. Wood, Thomas L. Griffiths:
Particle Filtering for Nonparametric Bayesian Matrix Factorization. NIPS 2006: 1513-1520 - [c16]Vikash K. Mansinghka, Charles Kemp, Thomas L. Griffiths, Joshua B. Tenenbaum:
Structured Priors for Structure Learning. UAI 2006 - [c15]Frank D. Wood, Thomas L. Griffiths, Zoubin Ghahramani:
A Non-Parametric Bayesian Method for Inferring Hidden Causes. UAI 2006 - 2005
- [c14]Sharon Goldwater, Thomas L. Griffiths, Mark Johnson:
Interpolating between types and tokens by estimating power-law generators. NIPS 2005: 459-466 - [c13]Thomas L. Griffiths, Zoubin Ghahramani:
Infinite latent feature models and the Indian buffet process. NIPS 2005: 475-482 - 2004
- [c12]Mark Steyvers, Padhraic Smyth, Michal Rosen-Zvi, Thomas L. Griffiths:
Probabilistic author-topic models for information discovery. KDD 2004: 306-315 - [c11]Thomas L. Griffiths, Mark Steyvers, David M. Blei, Joshua B. Tenenbaum:
Integrating Topics and Syntax. NIPS 2004: 537-544 - [c10]Tomoharu Iwata, Kazumi Saito, Naonori Ueda, Sean Stromsten, Thomas L. Griffiths, Joshua B. Tenenbaum:
Parametric Embedding for Class Visualization. NIPS 2004: 617-624 - [c9]Michal Rosen-Zvi, Thomas L. Griffiths, Mark Steyvers, Padhraic Smyth:
The Author-Topic Model for Authors and Documents. UAI 2004: 487-494 - 2003
- [c8]David M. Blei, Thomas L. Griffiths, Michael I. Jordan, Joshua B. Tenenbaum:
Hierarchical Topic Models and the Nested Chinese Restaurant Process. NIPS 2003: 17-24 - [c7]Charles Kemp, Thomas L. Griffiths, Sean Stromsten, Joshua B. Tenenbaum:
Semi-Supervised Learning with Trees. NIPS 2003: 257-264 - [c6]Thomas L. Griffiths, Joshua B. Tenenbaum:
From Algorithmic to Subjective Randomness. NIPS 2003: 953-960 - 2002
- [c5]Thomas L. Griffiths, Mark Steyvers:
Prediction and Semantic Association. NIPS 2002: 11-18 - [c4]Joshua B. Tenenbaum, Thomas L. Griffiths:
Theory-Based Causal Inference. NIPS 2002: 35-42 - [c3]David Danks, Thomas L. Griffiths, Joshua B. Tenenbaum:
Dynamical Causal Learning. NIPS 2002: 67-74 - 2001
- [c2]Thomas L. Griffiths, Joshua B. Tenenbaum:
Using Vocabulary Knowledge in Bayesian Multinomial Estimation. NIPS 2001: 1385-1392 - 2000
- [c1]Joshua B. Tenenbaum, Thomas L. Griffiths:
Structure Learning in Human Causal Induction. NIPS 2000: 59-65
Data and Artifacts
- 2019
- [d1]Project Jupyter, Douglas Blank, David Bourgin, Alexander Brown, Matthias Bussonnier, Jonathan Frederic, Brian E. Granger, Thomas L. Griffiths, Jessica B. Hamrick, Kyle Kelley, Michael Pacer, Logan Page, Fernando Pérez, Benjamin Ragan-Kelley, Jordan W. Suchow, Carol Willing:
nbgrader v0.5.5. Zenodo, 2019
Informal and Other Publications
- 2024
- [i131]Sunayana Rane, Polyphony J. Bruna, Ilia Sucholutsky, Christopher T. Kello, Thomas L. Griffiths:
Concept Alignment. CoRR abs/2401.08672 (2024) - [i130]Jian-Qiao Zhu, Thomas L. Griffiths:
Incoherent Probability Judgments in Large Language Models. CoRR abs/2401.16646 (2024) - [i129]Jian-Qiao Zhu, Haijiang Yan, Thomas L. Griffiths:
Recovering Mental Representations from Large Language Models with Markov Chain Monte Carlo. CoRR abs/2401.16657 (2024) - [i128]Andi Peng, Andreea Bobu, Belinda Z. Li, Theodore R. Sumers, Ilia Sucholutsky, Nishanth Kumar, Thomas L. Griffiths, Julie A. Shah:
Preference-Conditioned Language-Guided Abstraction. CoRR abs/2402.03081 (2024) - [i127]Sreejan Kumar, Raja Marjieh, Byron Zhang, Declan Campbell, Michael Y. Hu, Umang Bhatt, Brenden M. Lake, Thomas L. Griffiths:
Comparing Abstraction in Humans and Large Language Models Using Multimodal Serial Reproduction. CoRR abs/2402.03618 (2024) - [i126]Xuechunzi Bai, Angelina Wang, Ilia Sucholutsky, Thomas L. Griffiths:
Measuring Implicit Bias in Explicitly Unbiased Large Language Models. CoRR abs/2402.04105 (2024) - [i125]Declan Campbell, Sreejan Kumar, Tyler Giallanza, Thomas L. Griffiths, Jonathan D. Cohen:
Human-Like Geometric Abstraction in Large Pre-trained Neural Networks. CoRR abs/2402.04203 (2024) - [i124]Raja Marjieh, Pol van Rijn, Ilia Sucholutsky, Harin Lee, Thomas L. Griffiths, Nori Jacoby:
A Rational Analysis of the Speech-to-Song Illusion. CoRR abs/2402.06992 (2024) - [i123]Ioana Marinescu, R. Thomas McCoy, Thomas L. Griffiths:
Distilling Symbolic Priors for Concept Learning into Neural Networks. CoRR abs/2402.07035 (2024) - [i122]Ryan Liu, Theodore R. Sumers, Ishita Dasgupta, Thomas L. Griffiths:
How do Large Language Models Navigate Conflicts between Honesty and Helpfulness? CoRR abs/2402.07282 (2024) - [i121]Carlos G. Correa, Thomas L. Griffiths, Nathaniel D. Daw:
Program-Based Strategy Induction for Reinforcement Learning. CoRR abs/2402.16668 (2024) - [i120]Andi Peng, Ilia Sucholutsky, Belinda Z. Li, Theodore R. Sumers, Thomas L. Griffiths, Jacob Andreas, Julie A. Shah:
Learning with Language-Guided State Abstractions. CoRR abs/2402.18759 (2024) - [i119]Xudong Guo, Kaixuan Huang, Jiale Liu, Wenhui Fan, Natalia Vélez, Qingyun Wu, Huazheng Wang, Thomas L. Griffiths, Mengdi Wang:
Embodied LLM Agents Learn to Cooperate in Organized Teams. CoRR abs/2403.12482 (2024) - [i118]Allison Chen, Ilia Sucholutsky, Olga Russakovsky, Thomas L. Griffiths:
Analyzing the Roles of Language and Vision in Learning from Limited Data. CoRR abs/2403.19669 (2024) - [i117]Jian-Qiao Zhu, Haijiang Yan, Thomas L. Griffiths:
Language Models Trained to do Arithmetic Predict Human Risky and Intertemporal Choice. CoRR abs/2405.19313 (2024) - [i116]Raja Marjieh, Sreejan Kumar, Declan Campbell, Liyi Zhang, Gianluca M. Bencomo, Jake Snell, Thomas L. Griffiths:
Using Contrastive Learning with Generative Similarity to Learn Spaces that Capture Human Inductive Biases. CoRR abs/2405.19420 (2024) - [i115]Jian-Qiao Zhu, Thomas L. Griffiths:
Eliciting the Priors of Large Language Models using Iterated In-Context Learning. CoRR abs/2406.01860 (2024) - [i114]Liyi Zhang, Logan Nelson, Thomas L. Griffiths:
Analyzing the Benefits of Prototypes for Semi-Supervised Category Learning. CoRR abs/2406.02268 (2024) - [i113]Liyi Zhang, Michael Y. Li, Thomas L. Griffiths:
What Should Embeddings Embed? Autoregressive Models Represent Latent Generating Distributions. CoRR abs/2406.03707 (2024) - [i112]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 abs/2406.04302 (2024) - [i111]Ryan Liu, Jiayi Geng, Joshua C. Peterson, Ilia Sucholutsky, Thomas L. Griffiths:
Large Language Models Assume People are More Rational than We Really are. CoRR abs/2406.17055 (2024) - [i110]Akshara Prabhakar, Thomas L. Griffiths, R. Thomas McCoy:
Deciphering the Factors Influencing the Efficacy of Chain-of-Thought: Probability, Memorization, and Noisy Reasoning. CoRR abs/2407.01687 (2024) - [i109]Katherine M. Collins, Ilia Sucholutsky, Umang Bhatt, Kartik Chandra, Lionel Wong, Mina Lee, Cedegao E. Zhang, Tan Zhi-Xuan, Mark K. Ho, Vikash Mansinghka, Adrian Weller, Joshua B. Tenenbaum, Thomas L. Griffiths:
Building Machines that Learn and Think with People. CoRR abs/2408.03943 (2024) - [i108]Jian-Qiao Zhu, Joshua C. Peterson, Benjamin Enke, Thomas L. Griffiths:
Capturing the Complexity of Human Strategic Decision-Making with Machine Learning. CoRR abs/2408.07865 (2024) - [i107]Sebastian Musslick, Laura Bartlett, Suyog H. Chandramouli, Marina Dubova, Fernand Gobet, Thomas L. Griffiths, Jessica Hullman, Ross D. King, J. Nathan Kutz, Christopher G. Lucas, Suhas Mahesh, Franco Pestilli, Sabina J. Sloman, William R. Holmes:
Automating the Practice of Science - Opportunities, Challenges, and Implications. CoRR abs/2409.05890 (2024) - [i106]R. Thomas McCoy, Shunyu Yao, Dan Friedman, Mathew D. Hardy, Thomas L. Griffiths:
When a language model is optimized for reasoning, does it still show embers of autoregression? An analysis of OpenAI o1. CoRR abs/2410.01792 (2024) - 2023
- [i105]Ilia Sucholutsky, Thomas L. Griffiths:
Alignment with human representations supports robust few-shot learning. CoRR abs/2301.11990 (2023) - [i104]Raja Marjieh, Ilia Sucholutsky, Pol van Rijn, Nori Jacoby, Thomas L. Griffiths:
What Language Reveals about Perception: Distilling Psychophysical Knowledge from Large Language Models. CoRR abs/2302.01308 (2023) - [i103]Minkyu Shin, Jin Kim, Bas van Opheusden, Thomas L. Griffiths:
Superhuman Artificial Intelligence Can Improve Human Decision Making by Increasing Novelty. CoRR abs/2303.07462 (2023) - [i102]Michael Chang, Alyssa L. Dayan, Franziska Meier, Thomas L. Griffiths, Sergey Levine, Amy Zhang:
Neural Constraint Satisfaction: Hierarchical Abstraction for Combinatorial Generalization in Object Rearrangement. CoRR abs/2303.11373 (2023) - [i101]Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Thomas L. Griffiths, Yuan Cao, Karthik Narasimhan:
Tree of Thoughts: Deliberate Problem Solving with Large Language Models. CoRR abs/2305.10601 (2023) - [i100]R. Thomas McCoy, Thomas L. Griffiths:
Modeling rapid language learning by distilling Bayesian priors into artificial neural networks. CoRR abs/2305.14701 (2023) - [i99]Bhishma Dedhia, Michael Chang, Jake C. Snell, Thomas L. Griffiths, Niraj K. Jha:
Im-Promptu: In-Context Composition from Image Prompts. CoRR abs/2305.17262 (2023) - [i98]Zi Wang, Alexander Ku, Jason Baldridge, Thomas L. Griffiths, Been Kim:
Gaussian Process Probes (GPP) for Uncertainty-Aware Probing. CoRR abs/2305.18213 (2023) - [i97]Raja Marjieh, Nori Jacoby, Joshua C. Peterson, Thomas L. Griffiths:
The Universal Law of Generalization Holds for Naturalistic Stimuli. CoRR abs/2306.08564 (2023) - [i96]Theodore R. Sumers, Shunyu Yao, Karthik Narasimhan, Thomas L. Griffiths:
Cognitive Architectures for Language Agents. CoRR abs/2309.02427 (2023) - [i95]R. Thomas McCoy, Shunyu Yao, Dan Friedman, Matthew Hardy, Thomas L. Griffiths:
Embers of Autoregression: Understanding Large Language Models Through the Problem They are Trained to Solve. CoRR abs/2309.13638 (2023) - [i94]Ruiqi He, Carlos G. Correa, Thomas L. Griffiths, Mark K. Ho:
Structurally guided task decomposition in spatial navigation tasks. CoRR abs/2310.02221 (2023) - [i93]Kerem Oktar, Ilia Sucholutsky, Tania Lombrozo, Thomas L. Griffiths:
Dimensions of Disagreement: Unpacking Divergence and Misalignment in Cognitive Science and Artificial Intelligence. CoRR abs/2310.12994 (2023) - [i92]Ilia Sucholutsky, Lukas Muttenthaler, Adrian Weller, Andi Peng, Andreea Bobu, Been Kim, Bradley C. Love, Erin Grant, Jascha Achterberg, Joshua B. Tenenbaum, Katherine M. Collins, Katherine L. Hermann, Kerem Oktar, Klaus Greff, Martin N. Hebart, Nori Jacoby, Qiuyi Zhang, Raja Marjieh, Robert Geirhos, Sherol Chen, Simon Kornblith, Sunayana Rane, Talia Konkle, Thomas P. O'Connell, Thomas Unterthiner, Andrew K. Lampinen, Klaus-Robert Müller, Mariya Toneva, Thomas L. Griffiths:
Getting aligned on representational alignment. CoRR abs/2310.13018 (2023) - [i91]Sunayana Rane, Mark K. Ho, Ilia Sucholutsky, Thomas L. Griffiths:
Concept Alignment as a Prerequisite for Value Alignment. CoRR abs/2310.20059 (2023) - [i90]Ryan Liu, Howard Yen, Raja Marjieh, Thomas L. Griffiths, Ranjay Krishna:
Improving Interpersonal Communication by Simulating Audiences with Language Models. CoRR abs/2311.00687 (2023) - [i89]Yufei Tian, Abhilasha Ravichander, Lianhui Qin, Ronan Le Bras, Raja Marjieh, Nanyun Peng, Yejin Choi, Thomas L. Griffiths, Faeze Brahman:
MacGyver: Are Large Language Models Creative Problem Solvers? CoRR abs/2311.09682 (2023) - [i88]Thomas L. Griffiths, Jian-Qiao Zhu, Erin Grant, R. Thomas McCoy:
Bayes in the age of intelligent machines. CoRR abs/2311.10206 (2023) - [i87]Gianluca M. Bencomo, Jake C. Snell, Thomas L. Griffiths:
Implicit Maximum a Posteriori Filtering via Adaptive Optimization. CoRR abs/2311.10580 (2023) - [i86]Levin Brinkmann, Fabian Baumann, Jean-François Bonnefon, Maxime Derex, Thomas F. Müller, Anne-Marie Nussberger, Agnieszka Czaplicka, Alberto Acerbi, Thomas L. Griffiths, Joseph Henrich, Joel Z. Leibo, Richard McElreath, Pierre-Yves Oudeyer, Jonathan Stray, Iyad Rahwan:
Machine Culture. CoRR abs/2311.11388 (2023) - [i85]Jake C. Snell, Gianluca M. Bencomo, Thomas L. Griffiths:
A Metalearned Neural Circuit for Nonparametric Bayesian Inference. CoRR abs/2311.14601 (2023) - [i84]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 abs/2311.18644 (2023) - [i83]Qihong Lu, Tan T. Nguyen, Qiong Zhang, Uri Hasson, Thomas L. Griffiths, Jeffrey M. Zacks, Samuel J. Gershman, Kenneth A. Norman:
Toward a More Biologically Plausible Neural Network Model of Latent Cause Inference. CoRR abs/2312.08519 (2023) - [i82]Andrea Wynn, Ilia Sucholutsky, Thomas L. Griffiths:
Learning Human-like Representations to Enable Learning Human Values. CoRR abs/2312.14106 (2023) - [i81]Liyi Zhang, R. Thomas McCoy, Theodore R. Sumers, Jian-Qiao Zhu, Thomas L. Griffiths:
Deep de Finetti: Recovering Topic Distributions from Large Language Models. CoRR abs/2312.14226 (2023) - 2022
- [i80]Maya Malaviya, Ilia Sucholutsky, Kerem Oktar, Thomas L. Griffiths:
Can Humans Do Less-Than-One-Shot Learning? CoRR abs/2202.04670 (2022) - [i79]Raja Marjieh, Ilia Sucholutsky, Theodore R. Sumers, Nori Jacoby, Thomas L. Griffiths:
Predicting Human Similarity Judgments Using Large Language Models. CoRR abs/2202.04728 (2022) - [i78]Takateru Yamakoshi, Robert D. Hawkins, Thomas L. Griffiths:
Probing BERT's priors with serial reproduction chains. CoRR abs/2202.12226 (2022) - [i77]Sreejan Kumar, Ishita Dasgupta, Raja Marjieh, Nathaniel D. Daw, Jonathan D. Cohen, Thomas L. Griffiths:
Disentangling Abstraction from Statistical Pattern Matching in Human and Machine Learning. CoRR abs/2204.01437 (2022) - [i76]Theodore R. Sumers, Robert D. Hawkins, Mark K. Ho, Thomas L. Griffiths, Dylan Hadfield-Menell:
Linguistic communication as (inverse) reward design. CoRR abs/2204.05091 (2022) - [i75]Sreejan Kumar, Carlos G. Correa, Ishita Dasgupta, Raja Marjieh, Michael Y. Hu, Robert D. Hawkins, Nathaniel D. Daw, Jonathan D. Cohen, Karthik Narasimhan, Thomas L. Griffiths:
Using Natural Language and Program Abstractions to Instill Human Inductive Biases in Machines. CoRR abs/2205.11558 (2022) - [i74]Raja Marjieh, Pol van Rijn, Ilia Sucholutsky, Theodore R. Sumers, Harin Lee, Thomas L. Griffiths, Nori Jacoby:
Words are all you need? Capturing human sensory similarity with textual descriptors. CoRR abs/2206.04105 (2022) - [i73]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 abs/2206.07870 (2022) - [i72]Michael Chang, Thomas L. Griffiths, Sergey Levine:
Object Representations as Fixed Points: Training Iterative Refinement Algorithms with Implicit Differentiation. CoRR abs/2207.00787 (2022) - [i71]Sunayana Rane, Mira L. Nencheva, Zeyu Wang, Casey Lew-Williams, Olga Russakovsky, Thomas L. Griffiths:
Predicting Word Learning in Children from the Performance of Computer Vision Systems. CoRR abs/2207.09847 (2022) - [i70]Michael Y. Li, Erin Grant, Thomas L. Griffiths:
Gaussian process surrogate models for neural networks. CoRR abs/2208.06028 (2022) - [i69]Mathew D. Hardy, Bill D. Thompson, P. M. Krafft, Thomas L. Griffiths:
Bias amplification in experimental social networks is reduced by resampling. CoRR abs/2208.07261 (2022) - [i68]Raja Marjieh, Ilia Sucholutsky, Thomas A. Langlois, Nori Jacoby, Thomas L. Griffiths:
Analyzing Diffusion as Serial Reproduction. CoRR abs/2209.14821 (2022) - [i67]Ilia Sucholutsky, Raja Marjieh, Nori Jacoby, Thomas L. Griffiths:
On the Informativeness of Supervision Signals. CoRR abs/2211.01407 (2022) - [i66]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 abs/2211.03890 (2022) - 2021
- [i65]Stephan C. Meylan, Sathvik Nair, Thomas L. Griffiths:
Evaluating Models of Robust Word Recognition with Serial Reproduction. CoRR abs/2101.09788 (2021) - [i64]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 abs/2104.05857 (2021) - [i63]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 abs/2105.06546 (2021) - [i62]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 abs/2105.06948 (2021) - [i61]Shikhar Tuli, Ishita Dasgupta, Erin Grant, Thomas L. Griffiths:
Are Convolutional Neural Networks or Transformers more like human vision? CoRR abs/2105.07197 (2021) - [i60]Theodore R. Sumers, Robert X. D. Hawkins, Mark K. Ho, Thomas L. Griffiths:
Extending rational models of communication from beliefs to actions. CoRR abs/2105.11950 (2021) - [i59]Michael Chang, Sidhant Kaushik, Sergey Levine, Thomas L. Griffiths:
Modularity in Reinforcement Learning via Algorithmic Independence in Credit Assignment. CoRR abs/2106.14993 (2021) - [i58]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 abs/2107.07013 (2021) - [i57]Mark K. Ho, Thomas L. Griffiths:
Cognitive science as a source of forward and inverse models of human decisions for robotics and control. CoRR abs/2109.00127 (2021) - [i56]Ishita Dasgupta, Erin Grant, Thomas L. Griffiths:
Distinguishing rule- and exemplar-based generalization in learning systems. CoRR abs/2110.04328 (2021) - [i55]Samuel A. Barnett, Robert D. Hawkins, Thomas L. Griffiths:
A pragmatic account of the weak evidence effect. CoRR abs/2112.03799 (2021) - 2020
- [i54]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 abs/2002.01510 (2020) - [i53]Mark K. Ho, David Abel, Jonathan D. Cohen, Michael L. Littman, Thomas L. Griffiths:
The Efficiency of Human Cognition Reflects Planned Information Processing. CoRR abs/2002.05769 (2020) - [i52]Aditi Jha, Joshua Caleb Peterson, Thomas L. Griffiths:
Extracting low-dimensional psychological representations from convolutional neural networks. CoRR abs/2005.14363 (2020) - [i51]Ruairidh M. Battleday, Thomas L. Griffiths:
Analogy as Nonparametric Bayesian Inference over Relational Systems. CoRR abs/2006.04156 (2020) - [i50]R. Thomas McCoy, Erin Grant, Paul Smolensky, Thomas L. Griffiths, Tal Linzen:
Universal linguistic inductive biases via meta-learning. CoRR abs/2006.16324 (2020) - [i49]Michael Chang, Sidhant Kaushik, S. Matthew Weinberg, Thomas L. Griffiths, Sergey Levine:
Decentralized Reinforcement Learning: Global Decision-Making via Local Economic Transactions. CoRR abs/2007.02382 (2020) - [i48]Pulkit Singh, Joshua C. Peterson, Ruairidh M. Battleday, Thomas L. Griffiths:
End-to-end Deep Prototype and Exemplar Models for Predicting Human Behavior. CoRR abs/2007.08723 (2020) - [i47]Carlos G. Correa, Mark K. Ho, Fred Callaway, Thomas L. Griffiths:
Resource-rational Task Decomposition to Minimize Planning Costs. CoRR abs/2007.13862 (2020) - [i46]Thomas L. Griffiths:
Understanding Human Intelligence through Human Limitations. CoRR abs/2009.14050 (2020) - [i45]Theodore R. Sumers, Mark K. Ho, Robert X. D. Hawkins, Karthik Narasimhan, Thomas L. Griffiths:
Learning Rewards from Linguistic Feedback. CoRR abs/2009.14715 (2020) - [i44]Sreejan Kumar, Ishita Dasgupta, Jonathan D. Cohen, Nathaniel D. Daw, Thomas L. Griffiths:
Meta-Learning of Compositional Task Distributions in Humans and Machines. CoRR abs/2010.02317 (2020) - [i43]Robert X. D. Hawkins, Takateru Yamakoshi, Thomas L. Griffiths, Adele E. Goldberg:
Investigating representations of verb bias in neural language models. CoRR abs/2010.02375 (2020) - [i42]Rachit Dubey, Erin Grant, Michael Luo, Karthik Narasimhan, Thomas L. Griffiths:
Context-Conditioning as Cognitive Control: Guiding Meta-learning with Task Information. CoRR abs/2011.13782 (2020) - [i41]Aida Nematzadeh, Zahra Shekarchi, Thomas L. Griffiths, Suzanne Stevenson:
Competition in Cross-situational Word Learning: A Computational Study. CoRR abs/2012.03370 (2020) - [i40]Theodore R. Sumers, Mark K. Ho, Thomas L. Griffiths:
Show or Tell? Demonstration is More Robust to Changes in Shared Perception than Explanation. CoRR abs/2012.09035 (2020) - 2019
- [i39]Mayank Agrawal, Joshua C. Peterson, Thomas L. Griffiths:
Using Machine Learning to Guide Cognitive Modeling: A Case Study in Moral Reasoning. CoRR abs/1902.06744 (2019) - [i38]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 abs/1904.06866 (2019) - [i37]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 abs/1904.12690 (2019) - [i36]David D. Bourgin, Joshua C. Peterson, Daniel Reichman, Thomas L. Griffiths, Stuart J. Russell:
Cognitive Model Priors for Predicting Human Decisions. CoRR abs/1905.09397 (2019) - [i35]Mark K. Ho, Joanna Korman, Thomas L. Griffiths:
The Computational Structure of Unintentional Meaning. CoRR abs/1906.01983 (2019) - [i34]Joshua C. Peterson, Ruairidh M. Battleday, Thomas L. Griffiths, Olga Russakovsky:
Human uncertainty makes classification more robust. CoRR abs/1908.07086 (2019) - [i33]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 abs/1910.05789 (2019) - [i32]Mayank Agrawal, Joshua C. Peterson, Thomas L. Griffiths:
Scaling up Psychology via Scientific Regret Minimization: A Case Study in Moral Decision-Making. CoRR abs/1910.07581 (2019) - 2018
- [i31]Erin Grant, Chelsea Finn, Sergey Levine, Trevor Darrell, Thomas L. Griffiths:
Recasting Gradient-Based Meta-Learning as Hierarchical Bayes. CoRR abs/1801.08930 (2018) - [i30]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 abs/1802.01780 (2018) - [i29]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 abs/1802.05250 (2018) - [i28]Rachit Dubey, Pulkit Agrawal, Deepak Pathak, Thomas L. Griffiths, Alexei A. Efros:
Investigating Human Priors for Playing Video Games. CoRR abs/1802.10217 (2018) - [i27]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 abs/1805.07644 (2018) - [i26]Joshua C. Peterson, Paul Soulos, Aida Nematzadeh, Thomas L. Griffiths:
Learning Hierarchical Visual Representations in Deep Neural Networks Using Hierarchical Linguistic Labels. CoRR abs/1805.07647 (2018) - [i25]Jordan W. Suchow, Joshua C. Peterson, Thomas L. Griffiths:
Learning a face space for experiments on human identity. CoRR abs/1805.07653 (2018) - [i24]Michael Chang, Abhishek Gupta, Sergey Levine, Thomas L. Griffiths:
Automatically Composing Representation Transformations as a Means for Generalization. CoRR abs/1807.04640 (2018) - [i23]Sophia Sanborn, David D. Bourgin, Michael Chang, Thomas L. Griffiths:
Representational efficiency outweighs action efficiency in human program induction. CoRR abs/1807.07134 (2018) - [i22]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 abs/1808.02961 (2018) - [i21]Max Simchowitz, Kevin G. Jamieson, Jordan W. Suchow, Thomas L. Griffiths:
Adaptive Sampling for Convex Regression. CoRR abs/1808.04523 (2018) - [i20]Aida Nematzadeh, Kaylee Burns, Erin Grant, Alison Gopnik, Thomas L. Griffiths:
Evaluating Theory of Mind in Question Answering. CoRR abs/1808.09352 (2018) - [i19]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 abs/1809.02835 (2018) - [i18]Ghassen Jerfel, Erin Grant, Thomas L. Griffiths, Katherine A. Heller:
Online gradient-based mixtures for transfer modulation in meta-learning. CoRR abs/1812.06080 (2018) - 2017
- [i17]Stephan C. Meylan, Thomas L. Griffiths:
Word forms - not just their lengths- are optimized for efficient communication. CoRR abs/1703.01694 (2017) - [i16]Joshua C. Peterson, Thomas L. Griffiths:
Evidence for the size principle in semantic and perceptual domains. CoRR abs/1705.03260 (2017) - [i15]Rachit Dubey, Thomas L. Griffiths:
A rational analysis of curiosity. CoRR abs/1705.04351 (2017) - [i14]Dawn Chen, Joshua C. Peterson, Thomas L. Griffiths:
Evaluating vector-space models of analogy. CoRR abs/1705.04416 (2017) - [i13]Joshua C. Peterson, Joshua T. Abbott, Thomas L. Griffiths:
Leveraging deep neural networks to capture psychological representations. CoRR abs/1706.02417 (2017) - [i12]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 abs/1707.06354 (2017) - [i11]Ruairidh M. Battleday, Joshua C. Peterson, Thomas L. Griffiths:
Modeling Human Categorization of Natural Images Using Deep Feature Representations. CoRR abs/1711.04855 (2017) - [i10]Falk Lieder, Frederick Callaway, Sayan Gul, Paul M. Krueger, Thomas L. Griffiths:
Learning to select computations. CoRR abs/1711.06892 (2017) - 2016
- [i9]Baxter S. Eaves Jr., Naomi H. Feldman, Thomas L. Griffiths, Patrick Shafto:
Infant directed speech is consistent with teaching. CoRR abs/1606.01175 (2016) - [i8]Joshua C. Peterson, Joshua T. Abbott, Thomas L. Griffiths:
Adapting Deep Network Features to Capture Psychological Representations. CoRR abs/1608.02164 (2016) - [i7]Neil R. Bramley, Peter Dayan, Thomas L. Griffiths, David A. Lagnado:
Formalizing Neurath's Ship: Approximate Algorithms for Online Causal Learning. CoRR abs/1609.04212 (2016) - [i6]Jonathan D. Cohen, Biswadip Dey, Tom Griffiths, Sebastian Musslick, Kayhan Özcimder, Daniel Reichman, Igor Shinkar, Tal Wagner:
A Graph-Theoretic Approach to Multitasking. CoRR abs/1611.02400 (2016) - 2013
- [i5]Michael Pacer, Joseph Jay Williams, Xi Chen, Tania Lombrozo, Thomas L. Griffiths:
Evaluating computational models of explanation using human judgments. CoRR abs/1309.6855 (2013) - 2012
- [i4]Kurt T. Miller, Thomas L. Griffiths, Michael I. Jordan:
The Phylogenetic Indian Buffet Process: A Non-Exchangeable Nonparametric Prior for Latent Features. CoRR abs/1206.3279 (2012) - [i3]Vikash Mansinghka, Charles Kemp, Thomas L. Griffiths, Joshua B. Tenenbaum:
Structured Priors for Structure Learning. CoRR abs/1206.6852 (2012) - [i2]Frank D. Wood, Thomas L. Griffiths, Zoubin Ghahramani:
A Non-Parametric Bayesian Method for Inferring Hidden Causes. CoRR abs/1206.6865 (2012) - [i1]Michal Rosen-Zvi, Thomas L. Griffiths, Mark Steyvers, Padhraic Smyth:
The Author-Topic Model for Authors and Documents. CoRR abs/1207.4169 (2012)
Coauthor Index
David Bourgin
aka: David D. Bourgin
aka: David D. Bourgin
Frederick Callaway
aka: Fred Callaway
aka: Fred Callaway
Robert D. Hawkins
aka: Robert X. D. Hawkins
aka: Robert X. D. Hawkins
Joshua C. Peterson
aka: Joshua Caleb Peterson
aka: Joshua Caleb Peterson
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
Unpaywalled article links
Add open access links from 
to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the 
of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from 
, 
, and 
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from 
.
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2024-11-11 22:26 CET by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
