Thomas L. Griffiths

375 papers A* 26A 14B 55C 2Journal 230Unranked 48

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	A Rational Analysis of the Effects of Sycophantic AI. CoRR Rafael M. Batista, Thomas L. Griffiths
2026	J	`jnl`	AI Gamestore: Scalable, Open-Ended Evaluation of Machine General Intelligence with Human Games. CoRR Lance Ying, Ryan Truong, Prafull Sharma, Kaiya Ivy Zhao, Nathan Cloos, Kelsey R. Allen, Thomas L. Griffiths, Katherine M. Collins, José Hernández-Orallo, Phillip Isola, Samuel J. Gershman, Joshua B. Tenenbaum
2026	J	`jnl`	Cognitive Models and AI Algorithms Provide Templates for Designing Language Agents. CoRR Ryan Liu, Dilip Arumugam, Cedegao E. Zhang, Sean Escola, Xaq Pitkow, Thomas L. Griffiths
2026	J	`jnl`	Embodied LLM Agents Learn to Cooperate in Organized Teams. IEEE Trans. Comput. Soc. Syst. Xudong Guo, Kaixuan Huang, Jiale Liu, Wenhui Fan, Natalia Vélez, Qingyun Wu, Huazheng Wang, Thomas L. Griffiths, Mengdi Wang
2026	J	`jnl`	Language Model Teams as Distributed Systems. CoRR Elizabeth Mieczkowski, Katherine M. Collins, Ilia Sucholutsky, Natalia Vélez, Thomas L. Griffiths
2026	J	`jnl`	Parallelograms Strike Back: LLMs Generate Better Analogies than People. CoRR Qiawen Ella Liu, Raja Marjieh, Jian-Qiao Zhu, Adele E. Goldberg, Thomas L. Griffiths
2026	J	`jnl`	Serendipity by Design: Evaluating the Impact of Cross-domain Mappings on Human and LLM Creativity. CoRR Qiawen Ella Liu, Marina Dubova, Henry Conklin, Takumi Harada, Thomas L. Griffiths
2026	J	`jnl`	Why Human Guidance Matters in Collaborative Vibe Coding. CoRR Haoyu Hu, Raja Marjieh, Katherine M. Collins, Chenyi Li, Thomas L. Griffiths, Ilia Sucholutsky, Nori Jacoby
2025	J	`jnl`	A foundation model to predict and capture human cognition. Nat. Marcel Binz, Elif Akata, Matthias Bethge, Franziska Brändle, Fred Callaway, Julian Coda-Forno, Peter Dayan, Can Demircan, Maria K. Eckstein, Noémi Élteto, Thomas L. Griffiths, Susanne Haridi, Akshay K. Jagadish, Ji-An Li, Alexander Kipnis, Sreejan Kumar, Tobias Ludwig, Marvin Mathony, Marcelo G. Mattar, Alireza Modirshanechi, Surabhi S. Nath, Joshua C. Peterson, Milena Rmus, Evan M. Russek, Tankred Saanum, Johannes A. Schubert, Luca M. Schulze Buschoff, Nishad Singhi, Xin Sui, Mirko Thalmann, Fabian J. Theis, Vuong Truong, Vishaal Udandarao, Konstantinos Voudouris, Robert C. Wilson, Kristin Witte, Shuchen Wu, Dirk U. Wulff, Huadong Xiong, Eric Schulz
2025	J	`jnl`	Accumulating Context Changes the Beliefs of Language Models. CoRR Jiayi Geng, Howard Chen, Ryan Liu, Manoel Horta Ribeiro, Robb Willer, Graham Neubig, Thomas L. Griffiths
2025	J	`jnl`	Amortized Bayesian Meta-Learning for Low-Rank Adaptation of Large Language Models. CoRR Liyi Zhang, Jake Snell, Thomas L. Griffiths
2025	J	`jnl`	Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems. CoRR Jiayi Geng, Howard Chen, Dilip Arumugam, Thomas L. Griffiths
2025	J	`jnl`	Are Large Language Models Sensitive to the Motives Behind Communication? CoRR Addison J. Wu, Ryan Liu, Kerem Oktar, Theodore R. Sumers, Thomas L. Griffiths
2025	J	`jnl`	Assessing Adaptive World Models in Machines with Novel Games. CoRR Lance Ying, Katherine M. Collins, Prafull Sharma, Cédric Colas, Kaiya Ivy Zhao, Adrian Weller, Zenna Tavares, Phillip Isola, Samuel J. Gershman, Jacob Andreas, Thomas L. Griffiths, François Chollet, Kelsey R. Allen, Joshua B. Tenenbaum
2025	J	`jnl`	Causal Head Gating: A Framework for Interpreting Roles of Attention Heads in Transformers. CoRR Andrew Nam, Henry Conklin, Yukang Yang, Thomas L. Griffiths, Jonathan D. Cohen, Sarah-Jane Leslie
2025	J	`jnl`	Characterizing the Interaction of Cultural Evolution Mechanisms in Experimental Social Networks. CoRR Raja Marjieh, Manuel Anglada-Tort, Thomas L. Griffiths, Nori Jacoby
2025	J	`jnl`	Characterizing the Large-Scale Structure of Multimodal Semantic Networks. Cogn. Sci. Raja Marjieh, Pol van Rijn, Ilia Sucholutsky, Harin Lee, Nori Jacoby, Thomas L. Griffiths
2025	J	`jnl`	Cognitive Foundations for Reasoning and Their Manifestation in LLMs. CoRR Priyanka Kargupta, Shuyue Stella Li, Haocheng Wang, Jinu Lee, Shan Chen, Orevaoghene Ahia, Dean Light, Thomas L. Griffiths, Max Kleiman-Weiner, Jiawei Han, Asli Celikyilmaz, Yulia Tsvetkov
2025	A*	`conf`	Conformal Prediction as Bayesian Quadrature. ICML Jake C. Snell, Thomas L. Griffiths
2025	J	`jnl`	Conformal Prediction as Bayesian Quadrature. CoRR Jake C. Snell, Thomas L. Griffiths
2025	J	`jnl`	Convolutional Neural Networks Can (Meta-)Learn the Same-Different Relation. CoRR Max Gupta, Sunayana Rane, R. Thomas McCoy, Thomas L. Griffiths
2025	J	`jnl`	Demystifying the Mechanisms Behind Emergent Exploration in Goal-conditioned RL. CoRR Mahsa Bastankhah, Grace Liu, Dilip Arumugam, Thomas L. Griffiths, Benjamin Eysenbach
2025	J	`jnl`	Energy-Efficient Domain-Specific Artificial Intelligence Models and Agents: Pathways and Paradigms. CoRR Abhijit Chatterjee, Niraj K. Jha, Jonathan D. Cohen, Thomas L. Griffiths, Hongjing Lu, Diana Marculescu, Ashiqur Rasul, Keshab K. Parhi
2025	J	`jnl`	Evaluating Language Models' Evaluations of Games. CoRR Katherine M. Collins, Cedegao E. Zhang, Graham Todd, Lance Ying, Mauricio Barba da Costa, Ryan Liu, Prafull Sharma, Adrian Weller, Ionatan Kuperwajs, Lionel Wong, Joshua B. Tenenbaum, Thomas L. Griffiths
2025	—	`conf`	Evaluating distillation methods for data-efficient syntax learning. EMNLP (Findings) Takateru Yamakoshi, Thomas L. Griffiths, R. Thomas McCoy, Robert D. Hawkins
2025	J	`jnl`	Generation and Evaluation in the Human Invention Process through the Lens of Game Design. CoRR Katherine M. Collins, Graham Todd, Cedegao E. Zhang, Adrian Weller, Julian Togelius, Junyi Chu, Lionel Wong, Thomas L. Griffiths, Joshua B. Tenenbaum
2025	J	`jnl`	Getting aligned on representational alignment. Trans. Mach. Learn. Res. Ilia Sucholutsky, Lukas Muttenthaler, Adrian Weller, Andi Peng, Andreea Bobu, Been Kim, Bradley C. Love, Christopher J. Cueva, Erin Grant, Iris I. A. Groen, Jascha Achterberg, Joshua B. Tenenbaum, Katherine M. Collins, Katherine L. Hermann, Kerem Oktar, Klaus Greff, Martin N. Hebart, Nathan Cloos, Nikolaus Kriegeskorte, Nori Jacoby, Qiuyi Zhang, Raja Marjieh, Robert Geirhos, Sherol Chen, Simon Kornblith, Sunayana Rane, Talia Konkle, Thomas P. O'Connell, Thomas Unterthiner, Andrew Kyle Lampinen, Klaus-Robert Müller, Mariya Toneva, Thomas L. Griffiths
2025	A	`conf`	Hindsight Merging: Diverse Data Generation with Language Models. UAI Veniamin Veselovsky, Benedikt Stroebl, Gianluca M. Bencomo, Dilip Arumugam, Lisa Schut, Arvind Narayanan, Thomas L. Griffiths
2025	J	`jnl`	Identifying and Mitigating the Influence of the Prior Distribution in Large Language Models. CoRR Liyi Zhang, Veniamin Veselovsky, R. Thomas McCoy, Thomas L. Griffiths
2025	A*	`conf`	Language Models Trained to do Arithmetic Predict Human Risky and Intertemporal Choice. ICLR Jian-Qiao Zhu, Haijiang Yan, Thomas L. Griffiths
2025	A*	`conf`	Large Language Models Assume People are More Rational than We Really are. ICLR Ryan Liu, Jiayi Geng, Joshua C. Peterson, Ilia Sucholutsky, Thomas L. Griffiths
2025	J	`jnl`	Large Language Models Develop Novel Social Biases Through Adaptive Exploration. CoRR Addison J. Wu, Ryan Liu, Xuechunzi Bai, Thomas L. Griffiths
2025	J	`jnl`	Localized Cultural Knowledge is Conserved and Controllable in Large Language Models. CoRR Veniamin Veselovsky, Berke Argin, Benedikt Stroebl, Chris Wendler, Robert West, James Evans, Thomas L. Griffiths, Arvind Narayanan
2025	J	`jnl`	Machine Bullshit: Characterizing the Emergent Disregard for Truth in Large Language Models. CoRR Kaiqu Liang, Haimin Hu, Xuandong Zhao, Dawn Song, Thomas L. Griffiths, Jaime Fernández Fisac
2025	A*	`conf`	Mind Your Step (by Step): Chain-of-Thought can Reduce Performance on Tasks where Thinking Makes Humans Worse. ICML Ryan Liu, Jiayi Geng, Addison J. Wu, Ilia Sucholutsky, Tania Lombrozo, Thomas L. Griffiths
2025	J	`jnl`	On Benchmarking Human-Like Intelligence in Machines. CoRR Lance Ying, Katherine M. Collins, Lionel Wong, Ilia Sucholutsky, Ryan Liu, Adrian Weller, Tianmin Shu, Thomas L. Griffiths, Joshua B. Tenenbaum
2025	J	`jnl`	Partner Modelling Emerges in Recurrent Agents (But Only When It Matters). CoRR Ruaridh Mon-Williams, Max Taylor-Davies, Elizabeth Mieczkowski, Natalia Vélez, Neil R. Bramley, Yanwei Wang, Thomas L. Griffiths, Christopher G. Lucas
2025	J	`jnl`	People use fast, flat goal-directed simulation to reason about novel problems. CoRR Katherine M. Collins, Cedegao E. Zhang, Lionel Wong, Mauricio Barba da Costa, Graham Todd, Adrian Weller, Samuel J. Cheyette, Thomas L. Griffiths, Joshua B. Tenenbaum
2025	J	`jnl`	Predictability Shapes Adaptation: An Evolutionary Perspective on Modes of Learning in Transformers. CoRR Alexander Y. Ku, Thomas L. Griffiths, Stephanie C. Y. Chan
2025	J	`jnl`	Predicting Multi-Agent Specialization via Task Parallelizability. CoRR Elizabeth Mieczkowski, Ruaridh Mon-Williams, Neil Bramley, Christopher G. Lucas, Natalia Vélez, Thomas L. Griffiths
2025	J	`jnl`	RLHS: Mitigating Misalignment in RLHF with Hindsight Simulation. CoRR Kaiqu Liang, Haimin Hu, Ryan Liu, Thomas L. Griffiths, Jaime Fernández Fisac
2025	J	`jnl`	Recovering Event Probabilities from Large Language Model Embeddings via Axiomatic Constraints. CoRR Jian-Qiao Zhu, Haijiang Yan, Thomas L. Griffiths
2025	J	`jnl`	Steering Risk Preferences in Large Language Models by Aligning Behavioral and Neural Representations. CoRR Jian-Qiao Zhu, Haijiang Yan, Thomas L. Griffiths
2025	J	`jnl`	Teasing Apart Architecture and Initial Weights as Sources of Inductive Bias in Neural Networks. CoRR Gianluca M. Bencomo, Max Gupta, Ioana Marinescu, R. Thomas McCoy, Thomas L. Griffiths
2025	J	`jnl`	Time Spent Thinking in Online Chess Reflects the Value of Computation. Cogn. Sci. Evan M. Russek, Daniel Acosta-kane, Bas van Opheusden, Marcelo G. Mattar, Thomas L. Griffiths
2025	J	`jnl`	Toward Efficient Exploration by Large Language Model Agents. CoRR Dilip Arumugam, Thomas L. Griffiths
2025	—	`conf`	Towards Foundation Models for 3D Vision: How Close are We? 3DV Yiming Zuo, Karhan Kayan, Maggie Wang, Kevin Jeon, Jia Deng, Thomas L. Griffiths
2025	J	`jnl`	Understanding Task Representations in Neural Networks via Bayesian Ablation. CoRR Andrew Nam, Declan Campbell, Thomas L. Griffiths, Jonathan D. Cohen, Sarah-Jane Leslie
2025	J	`jnl`	Using Reinforcement Learning to Train Large Language Models to Explain Human Decisions. CoRR Jian-Qiao Zhu, Hanbo Xie, Dilip Arumugam, Robert C. Wilson, Thomas L. Griffiths
2025	J	`jnl`	Using the Tools of Cognitive Science to Understand Large Language Models at Different Levels of Analysis. CoRR Alexander Ku, Declan Campbell, Xuechunzi Bai, Jiayi Geng, Ryan Liu, Raja Marjieh, R. Thomas McCoy, Andrew Nam, Ilia Sucholutsky, Veniamin Veselovsky, Liyi Zhang, Jian-Qiao Zhu, Thomas L. Griffiths
2025	J	`jnl`	VideoGameBench: Can Vision-Language Models complete popular video games? CoRR Alex L. Zhang, Thomas L. Griffiths, Karthik R. Narasimhan, Ofir Press
2025	J	`jnl`	Visual serial processing deficits explain divergences in human and VLM reasoning. CoRR Nicholas Budny, Kia Ghods, Declan Campbell, Raja Marjieh, Amogh Joshi, Sreejan Kumar, Jonathan D. Cohen, Taylor W. Webb, Thomas L. Griffiths
2025	J	`jnl`	What Should Embeddings Embed? Autoregressive Models Represent Latent Generating Distributions. Trans. Mach. Learn. Res. Liyi Zhang, Michael Y. Li, R. Thomas McCoy, Theodore R. Sumers, Jian-Qiao Zhu, Thomas L. Griffiths
2025	J	`jnl`	What is a Number, That a Large Language Model May Know It? CoRR Raja Marjieh, Veniamin Veselovsky, Thomas L. Griffiths, Ilia Sucholutsky
2025	J	`jnl`	Whither symbols in the era of advanced neural networks? CoRR Thomas L. Griffiths, Brenden M. Lake, R. Thomas McCoy, Ellie Pavlick, Taylor W. Webb
2024	J	`jnl`	A Rational Analysis of the Speech-to-Song Illusion. CoRR Raja Marjieh, Pol van Rijn, Ilia Sucholutsky, Harin Lee, Thomas L. Griffiths, Nori Jacoby
2024	J	`jnl`	Analyzing the Benefits of Prototypes for Semi-Supervised Category Learning. CoRR Liyi Zhang, Logan Nelson, Thomas L. Griffiths
2024	J	`jnl`	Analyzing the Roles of Language and Vision in Learning from Limited Data. CoRR Allison Chen, Ilia Sucholutsky, Olga Russakovsky, Thomas L. Griffiths
2024	J	`jnl`	Automating the Practice of Science - Opportunities, Challenges, and Implications. CoRR 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
2024	J	`jnl`	Building Machines that Learn and Think with People. CoRR 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
2024	J	`jnl`	Capturing the Complexity of Human Strategic Decision-Making with Machine Learning. CoRR Jian-Qiao Zhu, Joshua C. Peterson, Benjamin Enke, Thomas L. Griffiths
2024	J	`jnl`	Centaur: a foundation model of human cognition. CoRR Marcel Binz, Elif Akata, Matthias Bethge, Franziska Brändle, Fred Callaway, Julian Coda-Forno, Peter Dayan, Can Demircan, Maria K. Eckstein, Noémi Élteto, Thomas L. Griffiths, Susanne Haridi, Akshay K. Jagadish, Ji-An Li, Alexander Kipnis, Sreejan Kumar, Tobias Ludwig, Marvin Mathony, Marcelo G. Mattar, Alireza Modirshanechi, Surabhi S. Nath, Joshua C. Peterson, Milena Rmus, Evan M. Russek, Tankred Saanum, Natalia Scharfenberg, Johannes A. Schubert, Luca M. Schulze Buschoff, Nishad Singhi, Xin Sui, Mirko Thalmann, Fabian J. Theis, Vuong Truong, Vishaal Udandarao, Konstantinos Voudouris, Robert C. Wilson, Kristin Witte, Shuchen Wu, Dirk U. Wulff, Huadong Xiong, Eric Schulz
2024	J	`jnl`	Cognitive Architectures for Language Agents. Trans. Mach. Learn. Res. Theodore R. Sumers, Shunyu Yao, Karthik Narasimhan, Thomas L. Griffiths
2024	J	`jnl`	Comparing Abstraction in Humans and Large Language Models Using Multimodal Serial Reproduction. CoRR Sreejan Kumar, Raja Marjieh, Byron Zhang, Declan Campbell, Michael Y. Hu, Umang Bhatt, Brenden M. Lake, Thomas L. Griffiths
2024	—	`conf`	Comparing Human Behavior to an Optimal Policy for Innovation. AAAI Spring Symposia Bonan Zhao, Natalia Vélez, Thomas L. Griffiths
2024	J	`jnl`	Concept Alignment. CoRR Sunayana Rane, Polyphony J. Bruna, Ilia Sucholutsky, Christopher T. Kello, Thomas L. Griffiths
2024	—	`conf`	Deciphering the Factors Influencing the Efficacy of Chain-of-Thought: Probability, Memorization, and Noisy Reasoning. EMNLP (Findings) Akshara Prabhakar, Thomas L. Griffiths, R. Thomas McCoy
2024	J	`jnl`	Deciphering the Factors Influencing the Efficacy of Chain-of-Thought: Probability, Memorization, and Noisy Reasoning. CoRR Akshara Prabhakar, Thomas L. Griffiths, R. Thomas McCoy
2024	J	`jnl`	Distilling Symbolic Priors for Concept Learning into Neural Networks. CoRR Ioana Marinescu, R. Thomas McCoy, Thomas L. Griffiths
2024	J	`jnl`	Eliciting the Priors of Large Language Models using Iterated In-Context Learning. CoRR Jian-Qiao Zhu, Thomas L. Griffiths
2024	J	`jnl`	Embodied LLM Agents Learn to Cooperate in Organized Teams. CoRR Xudong Guo, Kaixuan Huang, Jiale Liu, Wenhui Fan, Natalia Vélez, Qingyun Wu, Huazheng Wang, Thomas L. Griffiths, Mengdi Wang
2024	J	`jnl`	Erratum: Multitasking Capacity: Hardness Results and Improved Constructions. SIAM J. Discret. Math. Noga Alon, Jonathan D. Cohen, Thomas L. Griffiths, Pasin Manurangsi, Daniel Reichman, Igor Shinkar, Tal Wagner
2024	A*	`conf`	How do Large Language Models Navigate Conflicts between Honesty and Helpfulness? ICML Ryan Liu, Theodore R. Sumers, Ishita Dasgupta, Thomas L. Griffiths
2024	J	`jnl`	How do Large Language Models Navigate Conflicts between Honesty and Helpfulness? CoRR Ryan Liu, Theodore R. Sumers, Ishita Dasgupta, Thomas L. Griffiths
2024	J	`jnl`	Human-Like Geometric Abstraction in Large Pre-trained Neural Networks. CoRR Declan Campbell, Sreejan Kumar, Tyler Giallanza, Thomas L. Griffiths, Jonathan D. Cohen
2024	A*	`conf`	Implicit Maximum a Posteriori Filtering via Adaptive Optimization. ICLR Gianluca M. Bencomo, Jake Snell, Thomas L. Griffiths
2024	J	`jnl`	Incoherent Probability Judgments in Large Language Models. CoRR Jian-Qiao Zhu, Thomas L. Griffiths
2024	J	`jnl`	Inverting Cognitive Models With Neural Networks to Infer Preferences From Fixations. Cogn. Sci. Evan M. Russek, Frederick Callaway, Thomas L. Griffiths
2024	J	`jnl`	Language Models Trained to do Arithmetic Predict Human Risky and Intertemporal Choice. CoRR Jian-Qiao Zhu, Haijiang Yan, Thomas L. Griffiths
2024	J	`jnl`	Large Language Models Assume People are More Rational than We Really are. CoRR Ryan Liu, Jiayi Geng, Joshua C. Peterson, Ilia Sucholutsky, Thomas L. Griffiths
2024	A*	`conf`	Learning with Language-Guided State Abstractions. ICLR Andi Peng, Ilia Sucholutsky, Belinda Z. Li, Theodore R. Sumers, Thomas L. Griffiths, Jacob Andreas, Julie Shah
2024	J	`jnl`	Learning with Language-Guided State Abstractions. CoRR Andi Peng, Ilia Sucholutsky, Belinda Z. Li, Theodore R. Sumers, Thomas L. Griffiths, Jacob Andreas, Julie A. Shah
2024	—	`conf`	MacGyver: Are Large Language Models Creative Problem Solvers? NAACL-HLT Yufei Tian, Abhilasha Ravichander, Lianhui Qin, Ronan Le Bras, Raja Marjieh, Nanyun Peng, Yejin Choi, Thomas L. Griffiths, Faeze Brahman
2024	J	`jnl`	Measuring Implicit Bias in Explicitly Unbiased Large Language Models. CoRR Xuechunzi Bai, Angelina Wang, Ilia Sucholutsky, Thomas L. Griffiths
2024	J	`jnl`	Mind Your Step (by Step): Chain-of-Thought can Reduce Performance on Tasks where Thinking Makes Humans Worse. CoRR Ryan Liu, Jiayi Geng, Addison J. Wu, Ilia Sucholutsky, Tania Lombrozo, Thomas L. Griffiths
2024	A*	`conf`	Preference-Conditioned Language-Guided Abstraction. HRI Andi Peng, Andreea Bobu, Belinda Z. Li, Theodore R. Sumers, Ilia Sucholutsky, Nishanth Kumar, Thomas L. Griffiths, Julie A. Shah
2024	J	`jnl`	Preference-Conditioned Language-Guided Abstraction. CoRR Andi Peng, Andreea Bobu, Belinda Z. Li, Theodore R. Sumers, Ilia Sucholutsky, Nishanth Kumar, Thomas L. Griffiths, Julie A. Shah
2024	J	`jnl`	Program-Based Strategy Induction for Reinforcement Learning. CoRR Carlos G. Correa, Thomas L. Griffiths, Nathaniel D. Daw
2024	—	`conf`	Pushing the Limits of Learning from Limited Data. AAAI Spring Symposia Maya Malaviya, Ilia Sucholutsky, Thomas L. Griffiths
2024	J	`jnl`	Rational Metareasoning for Large Language Models. CoRR Nicolò De Sabbata, Theodore R. Sumers, Thomas L. Griffiths
2024	J	`jnl`	Recovering Mental Representations from Large Language Models with Markov Chain Monte Carlo. CoRR Jian-Qiao Zhu, Haijiang Yan, Thomas L. Griffiths
2024	J	`jnl`	Representational Alignment Supports Effective Machine Teaching. CoRR 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
2024	J	`jnl`	Towards Foundation Models for 3D Vision: How Close Are We? CoRR Yiming Zuo, Karhan Kayan, Maggie Wang, Kevin Jeon, Jia Deng, Thomas L. Griffiths
2024	J	`jnl`	Understanding the Limits of Vision Language Models Through the Lens of the Binding Problem. CoRR Declan Campbell, Sunayana Rane, Tyler Giallanza, Nicolò De Sabbata, Kia Ghods, Amogh Joshi, Alexander Ku, Steven M. Frankland, Thomas L. Griffiths, Jonathan D. Cohen, Taylor W. Webb
2024	J	`jnl`	Using Contrastive Learning with Generative Similarity to Learn Spaces that Capture Human Inductive Biases. CoRR Raja Marjieh, Sreejan Kumar, Declan Campbell, Liyi Zhang, Gianluca M. Bencomo, Jake Snell, Thomas L. Griffiths
2024	J	`jnl`	What Should Embeddings Embed? Autoregressive Models Represent Latent Generating Distributions. CoRR Liyi Zhang, Michael Y. Li, Thomas L. Griffiths
2024	J	`jnl`	When a language model is optimized for reasoning, does it still show embers of autoregression? An analysis of OpenAI o1. CoRR R. Thomas McCoy, Shunyu Yao, Dan Friedman, Mathew D. Hardy, Thomas L. Griffiths
2024	J	`jnl`	Word Forms Reflect Trade-Offs Between Speaker Effort and Robust Listener Recognition. Cogn. Sci. Stephan C. Meylan, Thomas L. Griffiths
2023	J	`jnl`	A Metalearned Neural Circuit for Nonparametric Bayesian Inference. CoRR Jake C. Snell, Gianluca M. Bencomo, Thomas L. Griffiths
2023	J	`jnl`	Alignment with human representations supports robust few-shot learning. CoRR Ilia Sucholutsky, Thomas L. Griffiths
2023	A*	`conf`	Analyzing Diffusion as Serial Reproduction. ICML Raja Marjieh, Ilia Sucholutsky, Thomas A. Langlois, Nori Jacoby, Thomas L. Griffiths
2023	J	`jnl`	Bayes in the age of intelligent machines. CoRR Thomas L. Griffiths, Jian-Qiao Zhu, Erin Grant, R. Thomas McCoy
2023	J	`jnl`	Cognitive Architectures for Language Agents. CoRR Theodore R. Sumers, Shunyu Yao, Karthik Narasimhan, Thomas L. Griffiths
2023	J	`jnl`	Concept Alignment as a Prerequisite for Value Alignment. CoRR Sunayana Rane, Mark K. Ho, Ilia Sucholutsky, Thomas L. Griffiths
2023	J	`jnl`	Deep de Finetti: Recovering Topic Distributions from Large Language Models. CoRR Liyi Zhang, R. Thomas McCoy, Theodore R. Sumers, Jian-Qiao Zhu, Thomas L. Griffiths
2023	J	`jnl`	Dimensions of Disagreement: Unpacking Divergence and Misalignment in Cognitive Science and Artificial Intelligence. CoRR Kerem Oktar, Ilia Sucholutsky, Tania Lombrozo, Thomas L. Griffiths
2023	J	`jnl`	Disentangling Abstraction from Statistical Pattern Matching in Human and Machine Learning. PLoS Comput. Biol. Sreejan Kumar, Ishita Dasgupta, Nathaniel D. Daw, Jonathan D. Cohen, Thomas L. Griffiths
2023	J	`jnl`	Embers of Autoregression: Understanding Large Language Models Through the Problem They are Trained to Solve. CoRR R. Thomas McCoy, Shunyu Yao, Dan Friedman, Matthew Hardy, Thomas L. Griffiths
2023	J	`jnl`	Exploring the hierarchical structure of human plans via program generation. CoRR Carlos G. Correa, Sophia Sanborn, Mark K. Ho, Frederick Callaway, Nathaniel D. Daw, Thomas L. Griffiths
2023	J	`jnl`	Extracting Low-Dimensional Psychological Representations from Convolutional Neural Networks. Cogn. Sci. Aditi Jha, Joshua C. Peterson, Thomas L. Griffiths
2023	J	`jnl`	Gaussian Process Probes (GPP) for Uncertainty-Aware Probing. CoRR Zi Wang, Alexander Ku, Jason Baldridge, Thomas L. Griffiths, Been Kim
2023	A	`conf`	Gaussian Process Surrogate Models for Neural Networks. UAI Michael Y. Li, Erin Grant, Thomas L. Griffiths
2023	J	`jnl`	Getting aligned on representational alignment. CoRR 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
2023	A*	`conf`	Hierarchical Abstraction for Combinatorial Generalization in Object Rearrangement. ICLR Michael Chang, Alyssa L. Dayan, Franziska Meier, Thomas L. Griffiths, Sergey Levine, Amy Zhang
2023	J	`jnl`	How do Humans Overcome Individual Computational Limitations by Working Together? Cogn. Sci. Natalia Vélez, Brian R. Christian, Mathew D. Hardy, Bill D. Thompson, Thomas L. Griffiths
2023	J	`jnl`	Humans decompose tasks by trading off utility and computational cost. PLoS Comput. Biol. Carlos G. Correa, Mark K. Ho, Frederick Callaway, Nathaniel D. Daw, Thomas L. Griffiths
2023	J	`jnl`	Im-Promptu: In-Context Composition from Image Prompts. CoRR Bhishma Dedhia, Michael Chang, Jake C. Snell, Thomas L. Griffiths, Niraj K. Jha
2023	J	`jnl`	Implicit Maximum a Posteriori Filtering via Adaptive Optimization. CoRR Gianluca M. Bencomo, Jake C. Snell, Thomas L. Griffiths
2023	J	`jnl`	Improving Interpersonal Communication by Simulating Audiences with Language Models. CoRR Ryan Liu, Howard Yen, Raja Marjieh, Thomas L. Griffiths, Ranjay Krishna
2023	J	`jnl`	Learning Human-like Representations to Enable Learning Human Values. CoRR Andrea Wynn, Ilia Sucholutsky, Thomas L. Griffiths
2023	J	`jnl`	Learning to Learn Functions. Cogn. Sci. Michael Y. Li, Fred Callaway, William D. Thompson, Ryan P. Adams, Thomas L. Griffiths
2023	J	`jnl`	MacGyver: Are Large Language Models Creative Problem Solvers? CoRR Yufei Tian, Abhilasha Ravichander, Lianhui Qin, Ronan Le Bras, Raja Marjieh, Nanyun Peng, Yejin Choi, Thomas L. Griffiths, Faeze Brahman
2023	J	`jnl`	Machine Culture. CoRR 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
2023	J	`jnl`	Modeling rapid language learning by distilling Bayesian priors into artificial neural networks. CoRR R. Thomas McCoy, Thomas L. Griffiths
2023	J	`jnl`	Neural Constraint Satisfaction: Hierarchical Abstraction for Combinatorial Generalization in Object Rearrangement. CoRR Michael Chang, Alyssa L. Dayan, Franziska Meier, Thomas L. Griffiths, Sergey Levine, Amy Zhang
2023	A	`conf`	On the informativeness of supervision signals. UAI Ilia Sucholutsky, Ruairidh M. Battleday, Katherine M. Collins, Raja Marjieh, Joshua C. Peterson, Pulkit Singh, Umang Bhatt, Nori Jacoby, Adrian Weller, Thomas L. Griffiths
2023	J	`jnl`	Structurally guided task decomposition in spatial navigation tasks. CoRR Ruiqi He, Carlos G. Correa, Thomas L. Griffiths, Mark K. Ho
2023	J	`jnl`	Superhuman Artificial Intelligence Can Improve Human Decision Making by Increasing Novelty. CoRR Minkyu Shin, Jin Kim, Bas van Opheusden, Thomas L. Griffiths
2023	J	`jnl`	The Computational Challenges of Means Selection Problems: Network Structure of Goal Systems Predicts Human Performance. Cogn. Sci. Daniel Reichman, Falk Lieder, David D. Bourgin, Nimrod Talmon, Thomas L. Griffiths
2023	J	`jnl`	The Universal Law of Generalization Holds for Naturalistic Stimuli. CoRR Raja Marjieh, Nori Jacoby, Joshua C. Peterson, Thomas L. Griffiths
2023	J	`jnl`	Toward a More Biologically Plausible Neural Network Model of Latent Cause Inference. CoRR Qihong Lu, Tan T. Nguyen, Qiong Zhang, Uri Hasson, Thomas L. Griffiths, Jeffrey M. Zacks, Samuel J. Gershman, Kenneth A. Norman
2023	J	`jnl`	Tree of Thoughts: Deliberate Problem Solving with Large Language Models. CoRR Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Thomas L. Griffiths, Yuan Cao, Karthik Narasimhan
2023	J	`jnl`	What Language Reveals about Perception: Distilling Psychophysical Knowledge from Large Language Models. CoRR Raja Marjieh, Ilia Sucholutsky, Pol van Rijn, Nori Jacoby, Thomas L. Griffiths
2023	A*	`conf`	Words are all you need? Language as an approximation for human similarity judgments. ICLR Raja Marjieh, Pol van Rijn, Ilia Sucholutsky, Theodore R. Sumers, Harin Lee, Thomas L. Griffiths, Nori Jacoby
2022	J	`jnl`	Analyzing Diffusion as Serial Reproduction. CoRR Raja Marjieh, Ilia Sucholutsky, Thomas A. Langlois, Nori Jacoby, Thomas L. Griffiths
2022	J	`jnl`	Bias amplification in experimental social networks is reduced by resampling. CoRR Mathew D. Hardy, Bill D. Thompson, P. M. Krafft, Thomas L. Griffiths
2022	J	`jnl`	Can Humans Do Less-Than-One-Shot Learning? CoRR Maya Malaviya, Ilia Sucholutsky, Kerem Oktar, Thomas L. Griffiths
2022	J	`jnl`	Cognitive Science as a Source of Forward and Inverse Models of Human Decisions for Robotics and Control. Annu. Rev. Control. Robotics Auton. Syst. Mark K. Ho, Thomas L. Griffiths
2022	J	`jnl`	Disentangling Abstraction from Statistical Pattern Matching in Human and Machine Learning. CoRR Sreejan Kumar, Ishita Dasgupta, Raja Marjieh, Nathaniel D. Daw, Jonathan D. Cohen, Thomas L. Griffiths
2022	J	`jnl`	Gaussian process surrogate models for neural networks. CoRR Michael Y. Li, Erin Grant, Thomas L. Griffiths
2022	J	`jnl`	How to talk so your robot will learn: Instructions, descriptions, and pragmatics. CoRR Theodore R. Sumers, Robert D. Hawkins, Mark K. Ho, Thomas L. Griffiths, Dylan Hadfield-Menell
2022	J	`jnl`	Humans decompose tasks by trading off utility and computational cost. CoRR Carlos G. Correa, Mark K. Ho, Frederick Callaway, Nathaniel D. Daw, Thomas L. Griffiths
2022	J	`jnl`	Linguistic communication as (inverse) reward design. CoRR Theodore R. Sumers, Robert D. Hawkins, Mark K. Ho, Thomas L. Griffiths, Dylan Hadfield-Menell
2022	J	`jnl`	Object Representations as Fixed Points: Training Iterative Refinement Algorithms with Implicit Differentiation. CoRR Michael Chang, Thomas L. Griffiths, Sergey Levine
2022	J	`jnl`	On the Informativeness of Supervision Signals. CoRR Ilia Sucholutsky, Raja Marjieh, Nori Jacoby, Thomas L. Griffiths
2022	J	`jnl`	Overcoming Individual Limitations Through Distributed Computation: Rational Information Accumulation in Multigenerational Populations. Top. Cogn. Sci. Mathew D. Hardy, Peaks M. Krafft, Bill Thompson, Thomas L. Griffiths
2022	J	`jnl`	Predicting Human Similarity Judgments Using Large Language Models. CoRR Raja Marjieh, Ilia Sucholutsky, Theodore R. Sumers, Nori Jacoby, Thomas L. Griffiths
2022	J	`jnl`	Predicting Word Learning in Children from the Performance of Computer Vision Systems. CoRR Sunayana Rane, Mira L. Nencheva, Zeyu Wang, Casey Lew-Williams, Olga Russakovsky, Thomas L. Griffiths
2022	—	`conf`	Probing BERT's priors with serial reproduction chains. ACL (Findings) Takateru Yamakoshi, Thomas L. Griffiths, Robert D. Hawkins
2022	J	`jnl`	Probing BERT's priors with serial reproduction chains. CoRR Takateru Yamakoshi, Robert D. Hawkins, Thomas L. Griffiths
2022	J	`jnl`	The pursuit of happiness: A reinforcement learning perspective on habituation and comparisons. PLoS Comput. Biol. Rachit Dubey, Thomas L. Griffiths, Peter Dayan
2022	J	`jnl`	Using Natural Language and Program Abstractions to Instill Human Inductive Biases in Machines. CoRR 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
2022	J	`jnl`	Words are all you need? Capturing human sensory similarity with textual descriptors. CoRR Raja Marjieh, Pol van Rijn, Ilia Sucholutsky, Theodore R. Sumers, Harin Lee, Thomas L. Griffiths, Nori Jacoby
2021	J	`jnl`	A pragmatic account of the weak evidence effect. CoRR Samuel A. Barnett, Robert D. Hawkins, Thomas L. Griffiths
2021	J	`jnl`	Are Convolutional Neural Networks or Transformers more like human vision? CoRR Shikhar Tuli, Ishita Dasgupta, Erin Grant, Thomas L. Griffiths
2021	J	`jnl`	Cognitive science as a source of forward and inverse models of human decisions for robotics and control. CoRR Mark K. Ho, Thomas L. Griffiths
2021	J	`jnl`	Control of mental representations in human planning. CoRR Mark K. Ho, David Abel, Carlos G. Correa, Michael L. Littman, Jonathan D. Cohen, Thomas L. Griffiths
2021	J	`jnl`	Distinguishing rule- and exemplar-based generalization in learning systems. CoRR Ishita Dasgupta, Erin Grant, Thomas L. Griffiths
2021	J	`jnl`	Evaluating Models of Robust Word Recognition with Serial Reproduction. CoRR Stephan C. Meylan, Sathvik Nair, Thomas L. Griffiths
2021	J	`jnl`	Extending rational models of communication from beliefs to actions. CoRR Theodore R. Sumers, Robert X. D. Hawkins, Mark K. Ho, Thomas L. Griffiths
2021	J	`jnl`	Fixation patterns in simple choice reflect optimal information sampling. PLoS Comput. Biol. Frederick Callaway, Antonio Rangel, Thomas L. Griffiths
2021	J	`jnl`	From partners to populations: A hierarchical Bayesian account of coordination and convention. CoRR Robert X. D. Hawkins, Michael Franke, Michael C. Frank, Kenny Smith, Thomas L. Griffiths, Noah D. Goodman
2021	A*	`conf`	Learning Rewards From Linguistic Feedback. AAAI Theodore R. Sumers, Mark K. Ho, Robert X. D. Hawkins, Karthik Narasimhan, Thomas L. Griffiths
2021	A*	`conf`	Meta-Learning of Structured Task Distributions in Humans and Machines. ICLR Sreejan Kumar, Ishita Dasgupta, Jonathan D. Cohen, Nathaniel D. Daw, Thomas L. Griffiths
2021	J	`jnl`	Modularity in Reinforcement Learning via Algorithmic Independence in Credit Assignment. CoRR Michael Chang, Sidhant Kaushik, Sergey Levine, Thomas L. Griffiths
2021	A*	`conf`	Passive attention in artificial neural networks predicts human visual selectivity. NeurIPS Thomas A. Langlois, H. Charles Zhao, Erin Grant, Ishita Dasgupta, Thomas L. Griffiths, Nori Jacoby
2021	J	`jnl`	Passive attention in artificial neural networks predicts human visual selectivity. CoRR Thomas A. Langlois, H. Charles Zhao, Erin Grant, Ishita Dasgupta, Thomas L. Griffiths, Nori Jacoby
2021	J	`jnl`	Shades of confusion: Lexical uncertainty modulates ad hoc coordination in an interactive communication task. CoRR Sonia K. Murthy, Robert X. D. Hawkins, Thomas L. Griffiths
2021	J	`jnl`	The Challenges of Large-Scale, Web-Based Language Datasets: Word Length and Predictability Revisited. Cogn. Sci. Stephan C. Meylan, Thomas L. Griffiths
2020	J	`jnl`	Analogy as Nonparametric Bayesian Inference over Relational Systems. CoRR Ruairidh M. Battleday, Thomas L. Griffiths
2020	J	`jnl`	Assessing Mathematics Misunderstandings via Bayesian Inverse Planning. Cogn. Sci. Anna N. Rafferty, Rachel Jansen, Thomas L. Griffiths
2020	J	`jnl`	Competition in Cross-situational Word Learning: A Computational Study. CoRR Aida Nematzadeh, Zahra Shekarchi, Thomas L. Griffiths, Suzanne Stevenson
2020	J	`jnl`	Context-Conditioning as Cognitive Control: Guiding Meta-learning with Task Information. CoRR Rachit Dubey, Erin Grant, Michael Luo, Karthik Narasimhan, Thomas L. Griffiths
2020	J	`jnl`	Decentralized Reinforcement Learning: Global Decision-Making via Local Economic Transactions. CoRR Michael Chang, Sidhant Kaushik, S. Matthew Weinberg, Thomas L. Griffiths, Sergey Levine
2020	J	`jnl`	End-to-end Deep Prototype and Exemplar Models for Predicting Human Behavior. CoRR Pulkit Singh, Joshua C. Peterson, Ruairidh M. Battleday, Thomas L. Griffiths
2020	J	`jnl`	Extracting low-dimensional psychological representations from convolutional neural networks. CoRR Aditi Jha, Joshua Caleb Peterson, Thomas L. Griffiths
2020	J	`jnl`	Generalizing meanings from partners to populations: Hierarchical inference supports convention formation on networks. CoRR Robert X. D. Hawkins, Noah D. Goodman, Adele E. Goldberg, Thomas L. Griffiths
2020	J	`jnl`	How to Be Helpful to Multiple People at Once. Cogn. Sci. Vael Gates, Thomas L. Griffiths, Anca D. Dragan
2020	—	`conf`	Investigating representations of verb bias in neural language models. EMNLP (1) Robert X. D. Hawkins, Takateru Yamakoshi, Thomas L. Griffiths, Adele E. Goldberg
2020	J	`jnl`	Investigating representations of verb bias in neural language models. CoRR Robert X. D. Hawkins, Takateru Yamakoshi, Thomas L. Griffiths, Adele E. Goldberg
2020	J	`jnl`	Learning Rewards from Linguistic Feedback. CoRR Theodore R. Sumers, Mark K. Ho, Robert X. D. Hawkins, Karthik Narasimhan, Thomas L. Griffiths
2020	J	`jnl`	Meta-Learning of Compositional Task Distributions in Humans and Machines. CoRR Sreejan Kumar, Ishita Dasgupta, Jonathan D. Cohen, Nathaniel D. Daw, Thomas L. Griffiths
2020	J	`jnl`	Multitasking Capacity: Hardness Results and Improved Constructions. SIAM J. Discret. Math. Noga Alon, Jonathan D. Cohen, Thomas L. Griffiths, Pasin Manurangsi, Daniel Reichman, Igor Shinkar, Tal Wagner, Alexander Y. Ku
2020	A*	`conf`	People Do Not Just Plan, They Plan to Plan. AAAI Mark K. Ho, David Abel, Jonathan D. Cohen, Michael L. Littman, Thomas L. Griffiths
2020	J	`jnl`	Resource-rational Task Decomposition to Minimize Planning Costs. CoRR Carlos G. Correa, Mark K. Ho, Fred Callaway, Thomas L. Griffiths
2020	J	`jnl`	Show or Tell? Demonstration is More Robust to Changes in Shared Perception than Explanation. CoRR Theodore R. Sumers, Mark K. Ho, Thomas L. Griffiths
2020	J	`jnl`	The Efficiency of Human Cognition Reflects Planned Information Processing. CoRR Mark K. Ho, David Abel, Jonathan D. Cohen, Michael L. Littman, Thomas L. Griffiths
2020	J	`jnl`	Understanding Human Intelligence through Human Limitations. CoRR Thomas L. Griffiths
2020	J	`jnl`	Universal linguistic inductive biases via meta-learning. CoRR R. Thomas McCoy, Erin Grant, Paul Smolensky, Thomas L. Griffiths, Tal Linzen
2019	—	`conf`	Automatically Composing Representation Transformations as a Means for Generalization. ICLR (Poster) Michael Chang, Abhishek Gupta, Sergey Levine, Thomas L. Griffiths
2019	J	`jnl`	Capturing human categorization of natural images at scale by combining deep networks and cognitive models. CoRR Ruairidh M. Battleday, Joshua C. Peterson, Thomas L. Griffiths
2019	J	`jnl`	Cognitive Model Priors for Predicting Human Decisions. CoRR David D. Bourgin, Joshua C. Peterson, Daniel Reichman, Thomas L. Griffiths, Stuart J. Russell
2019	A*	`conf`	Cognitive model priors for predicting human decisions. ICML David D. Bourgin, Joshua C. Peterson, Daniel Reichman, Stuart J. Russell, Thomas L. Griffiths
2019	A*	`conf`	Human Uncertainty Makes Classification More Robust. ICCV Joshua C. Peterson, Ruairidh M. Battleday, Thomas L. Griffiths, Olga Russakovsky
2019	J	`jnl`	Human uncertainty makes classification more robust. CoRR Joshua C. Peterson, Ruairidh M. Battleday, Thomas L. Griffiths, Olga Russakovsky
2019	J	`jnl`	Learning How to Generalize. Cogn. Sci. Joseph L. Austerweil, Sophia Sanborn, Thomas L. Griffiths
2019	J	`jnl`	On the Utility of Learning about Humans for Human-AI Coordination. CoRR Micah Carroll, Rohin Shah, Mark K. Ho, Thomas L. Griffiths, Sanjit A. Seshia, Pieter Abbeel, Anca D. Dragan
2019	J	`jnl`	Predicting human decisions with behavioral theories and machine learning. CoRR 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
2019	J	`jnl`	Scaling up Psychology via Scientific Regret Minimization: A Case Study in Moral Decision-Making. CoRR Mayank Agrawal, Joshua C. Peterson, Thomas L. Griffiths
2019	J	`jnl`	The Computational Structure of Unintentional Meaning. CoRR Mark K. Ho, Joanna Korman, Thomas L. Griffiths
2019	J	`jnl`	Using Machine Learning to Guide Cognitive Modeling: A Case Study in Moral Reasoning. CoRR Mayank Agrawal, Joshua C. Peterson, Thomas L. Griffiths
2018	J	`jnl`	Adaptive Sampling for Convex Regression. CoRR Max Simchowitz, Kevin Jamieson, Jordan W. Suchow, Thomas L. Griffiths
2018	J	`jnl`	Automatically Composing Representation Transformations as a Means for Generalization. CoRR Michael Chang, Abhishek Gupta, Sergey Levine, Thomas L. Griffiths
2018	J	`jnl`	Capturing human category representations by sampling in deep feature spaces. CoRR Joshua C. Peterson, Jordan W. Suchow, Krisha Aghi, Alexander Y. Ku, Thomas L. Griffiths
2018	J	`jnl`	Evaluating (and Improving) the Correspondence Between Deep Neural Networks and Human Representations. Cogn. Sci. Joshua C. Peterson, Joshua T. Abbott, Thomas L. Griffiths
2018	A*	`conf`	Evaluating Theory of Mind in Question Answering. EMNLP Aida Nematzadeh, Kaylee Burns, Erin Grant, Alison Gopnik, Thomas L. Griffiths
2018	J	`jnl`	Evaluating Theory of Mind in Question Answering. CoRR Aida Nematzadeh, Kaylee Burns, Erin Grant, Alison Gopnik, Thomas L. Griffiths
2018	—	`conf`	Exploiting Attention to Reveal Shortcomings in Memory Models. BlackboxNLP@EMNLP Kaylee Burns, Aida Nematzadeh, Erin Grant, Alison Gopnik, Thomas L. Griffiths
2018	J	`jnl`	Exploiting Effective Representations for Chinese Sentiment Analysis Using a Multi-Channel Convolutional Neural Network. CoRR Pengfei Liu, Ji Zhang, Cane Wing-Ki Leung, Chao He, Thomas L. Griffiths
2018	J	`jnl`	Generating Plans that Predict Themselves. CoRR Jaime F. Fisac, Chang Liu, Jessica B. Hamrick, S. Shankar Sastry, J. Karl Hedrick, Thomas L. Griffiths, Anca D. Dragan
2018	J	`jnl`	Goal Inference Improves Objective and Perceived Performance in Human-Robot Collaboration. CoRR Chang Liu, Jessica B. Hamrick, Jaime F. Fisac, Anca D. Dragan, J. Karl Hedrick, S. Shankar Sastry, Thomas L. Griffiths
2018	—	`conf`	Investigating Human Priors for Playing Video Games. ICLR (Workshop) Rachit Dubey, Pulkit Agrawal, Deepak Pathak, Alyosha A. Efros, Thomas L. Griffiths
2018	J	`jnl`	Investigating Human Priors for Playing Video Games. CoRR Rachit Dubey, Pulkit Agrawal, Deepak Pathak, Thomas L. Griffiths, Alexei A. Efros
2018	J	`jnl`	Learning Hierarchical Visual Representations in Deep Neural Networks Using Hierarchical Linguistic Labels. CoRR Joshua C. Peterson, Paul Soulos, Aida Nematzadeh, Thomas L. Griffiths
2018	J	`jnl`	Learning a face space for experiments on human identity. CoRR Jordan W. Suchow, Joshua C. Peterson, Thomas L. Griffiths
2018	A	`conf`	Learning to select computations. UAI Frederick Callaway, Sayan Gul, Paul M. Krueger, Thomas L. Griffiths, Falk Lieder
2018	J	`jnl`	Multitasking Capacity: Hardness Results and Improved Constructions. CoRR Noga Alon, Jonathan D. Cohen, Thomas L. Griffiths, Pasin Manurangsi, Daniel Reichman, Igor Shinkar, Tal Wagner, Alexander Y. Ku
2018	J	`jnl`	Online gradient-based mixtures for transfer modulation in meta-learning. CoRR Ghassen Jerfel, Erin Grant, Thomas L. Griffiths, Katherine A. Heller
2018	J	`jnl`	Rational metareasoning and the plasticity of cognitive control. PLoS Comput. Biol. Falk Lieder, Amitai Shenhav, Sebastian Musslick, Thomas L. Griffiths
2018	—	`conf`	Recasting Gradient-Based Meta-Learning as Hierarchical Bayes. ICLR (Poster) Erin Grant, Chelsea Finn, Sergey Levine, Trevor Darrell, Thomas L. Griffiths
2018	J	`jnl`	Recasting Gradient-Based Meta-Learning as Hierarchical Bayes. CoRR Erin Grant, Chelsea Finn, Sergey Levine, Trevor Darrell, Thomas L. Griffiths
2018	J	`jnl`	Representational efficiency outweighs action efficiency in human program induction. CoRR Sophia Sanborn, David D. Bourgin, Michael Chang, Thomas L. Griffiths
2018	J	`jnl`	Sensitivity to Shared Information in Social Learning. Cogn. Sci. Andrew Whalen, Thomas L. Griffiths, Daphna Buchsbaum
2017	J	`jnl`	A rational analysis of curiosity. CoRR Rachit Dubey, Thomas L. Griffiths
2017	A*	`conf`	Adapting Deep Network Features to Capture Psychological Representations: An Abridged Report. IJCAI Joshua C. Peterson, Joshua T. Abbott, Thomas L. Griffiths
2017	B	`conf`	Empirical tests of large-scale collaborative recall. CogSci Monica A. Gates, Jordan W. Suchow, Thomas L. Griffiths
2017	B	`conf`	Evaluating Vector-Space Models of Word Representation, or, The Unreasonable Effectiveness of Counting Words Near Other Words. CogSci Aida Nematzadeh, Stephan C. Meylan, Thomas L. Griffiths
2017	J	`jnl`	Evaluating vector-space models of analogy. CoRR Dawn Chen, Joshua C. Peterson, Thomas L. Griffiths
2017	B	`conf`	Evidence for the size principle in semantic and perceptual domains. CogSci Joshua C. Peterson, Thomas L. Griffiths
2017	J	`jnl`	Evidence for the size principle in semantic and perceptual domains. CoRR Joshua C. Peterson, Thomas L. Griffiths
2017	B	`conf`	How Can Memory-Augmented Neural Networks Pass a False-Belief Task? CogSci Erin Grant, Aida Nematzadeh, Thomas L. Griffiths
2017	J	`jnl`	Learning to select computations. CoRR Falk Lieder, Frederick Callaway, Sayan Gul, Paul M. Krueger, Thomas L. Griffiths
2017	J	`jnl`	Leveraging deep neural networks to capture psychological representations. CoRR Joshua C. Peterson, Joshua T. Abbott, Thomas L. Griffiths
2017	J	`jnl`	Modeling Human Categorization of Natural Images Using Deep Feature Representations. CoRR Ruairidh M. Battleday, Joshua C. Peterson, Thomas L. Griffiths
2017	C	`conf`	Pragmatic-Pedagogic Value Alignment. ISRR 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
2017	J	`jnl`	Pragmatic-Pedagogic Value Alignment. CoRR 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
2017	B	`conf`	Uncovering visual priors in spatial memory using serial reproduction. CogSci Thomas A. Langlois, Nori Jacoby, Jordan W. Suchow, Thomas L. Griffiths
2017	A*	`conf`	When Does Bounded-Optimal Metareasoning Favor Few Cognitive Systems? AAAI Smitha Milli, Falk Lieder, Thomas L. Griffiths
2017	J	`jnl`	Word forms - not just their lengths- are optimized for efficient communication. CoRR Stephan C. Meylan, Thomas L. Griffiths
2016	B	`conf`	Adapting Deep Network Features to Capture Psychological Representations. CogSci Joshua C. Peterson, Joshua T. Abbott, Thomas L. Griffiths
2016	J	`jnl`	Adapting Deep Network Features to Capture Psychological Representations. CoRR Joshua C. Peterson, Joshua T. Abbott, Thomas L. Griffiths
2016	B	`conf`	Design from Zeroth Principles. CogSci Jordan W. Suchow, Michael D. Pacer, Thomas L. Griffiths
2016	J	`jnl`	Exploring Human Cognition Using Large Image Databases. Top. Cogn. Sci. Thomas L. Griffiths, Joshua T. Abbott, Anne S. Hsu
2016	J	`jnl`	Faster Teaching via POMDP Planning. Cogn. Sci. Anna N. Rafferty, Emma Brunskill, Thomas L. Griffiths, Patrick Shafto
2016	J	`jnl`	Formalizing Neurath's Ship: Approximate Algorithms for Online Causal Learning. CoRR Neil R. Bramley, Peter Dayan, Thomas L. Griffiths, David A. Lagnado
2016	C	`conf`	Generating Plans that Predict Themselves. WAFR Jaime F. Fisac, Chang Liu, Jessica B. Hamrick, Shankar Sastry, J. Karl Hedrick, Thomas L. Griffiths, Anca D. Dragan
2016	A	`conf`	Goal Inference Improves Objective and Perceived Performance in Human-Robot Collaboration. AAMAS Chang Liu, Jessica B. Hamrick, Jaime F. Fisac, Anca D. Dragan, J. Karl Hedrick, S. Shankar Sastry, Thomas L. Griffiths
2016	J	`jnl`	Infant directed speech is consistent with teaching. CoRR Baxter S. Eaves Jr., Naomi H. Feldman, Thomas L. Griffiths, Patrick Shafto
2016	J	`jnl`	Natural speech reveals the semantic maps that tile human cerebral cortex. Nat. Alexander G. Huth, Wendy A. de Heer, Thomas L. Griffiths, Frédéric E. Theunissen, Jack L. Gallant
2016	B	`conf`	The Sapir-Whorf Hypothesis and Probabilistic Inference: Evidence from the Domain of Color. CogSci Emily Cibelli, Yang Xu, Joseph L. Austerweil, Thomas L. Griffiths, Terry Regier
2016	B	`conf`	Using Inverse Planning for Personalized Feedback. EDM Anna N. Rafferty, Rachel Jansen, Thomas L. Griffiths
2016	B	`conf`	Wallace: Automating Cultural Evolution Experiments Through Crowdsourcing. CogSci Jordan W. Suchow, Thomas J. H. Morgan, Jessica B. Hamrick, Michael D. Pacer, Stephan C. Meylan, Thomas L. Griffiths
2015	B	`conf`	A Bayesian Framework for Learning Words From Multiword Utterances. CogSci Stephan C. Meylan, Thomas L. Griffiths
2015	B	`conf`	Can children balance the size of a majority with the quality of their information? CogSci Jane C. Hu, Andrew Whalen, Daphna Buchsbaum, Thomas L. Griffiths, Fei Xu
2015	B	`conf`	Children and adults differ in their strategies for social learning. CogSci Falk Lieder, Zi Lin Sim, Jane C. Hu, Thomas L. Griffiths, Fei Xu
2015	B	`conf`	Children search for information as efficiently as adults, but seek additional confirmatory evidence. CogSci Azzurra Ruggeri, Tania Lombrozo, Thomas L. Griffiths, Fei Xu
2015	B	`conf`	Generative and Discriminative Models in Cognitive Science. CogSci Bradley C. Love, Michael Ramscar, Thomas L. Griffiths, Matt Jones
2015	J	`jnl`	Inferring Learners' Knowledge From Their Actions. Cogn. Sci. Anna N. Rafferty, Michelle M. LaMar, Thomas L. Griffiths
2015	A	`conf`	Interpreting Freeform Equation Solving. AIED Anna N. Rafferty, Thomas L. Griffiths
2015	J	`jnl`	Rational Use of Cognitive Resources: Levels of Analysis Between the Computational and the Algorithmic. Top. Cogn. Sci. Thomas L. Griffiths, Falk Lieder, Noah D. Goodman
2015	B	`conf`	Think again? The amount of mental simulation tracks uncertainty in the outcome. CogSci Jessica B. Hamrick, Kevin A. Smith, Thomas L. Griffiths, Ed Vul
2015	B	`conf`	Upsetting the contingency table: Causal induction over sequences of point events. CogSci Michael Pacer, Thomas L. Griffiths
2015	B	`conf`	What the Baldwin Effect affects. CogSci Thomas J. H. Morgan, Thomas L. Griffiths
2015	B	`conf`	When to use which heuristic: A rational solution to the strategy selection problem. CogSci Falk Lieder, Thomas L. Griffiths
2014	B	`conf`	A Bounded Rationality Account of Wishful Thinking. CogSci Rebecca Neumann, Anna N. Rafferty, Thomas L. Griffiths
2014	—	`conf`	Algorithm selection by rational metareasoning as a model of human strategy selection. NIPS Falk Lieder, Dillon Plunkett, Jessica B. Hamrick, Stuart J. Russell, Nicholas Hay, Thomas L. Griffiths
2014	J	`jnl`	Analyzing the Rate at Which Languages Lose the Influence of a Common Ancestor. Cogn. Sci. Anna N. Rafferty, Thomas L. Griffiths, Dan Klein
2014	B	`conf`	Caching Algorithms and Rational Models of Memory. CogSci Avi Press, Michael Pacer, Thomas L. Griffiths, Brian R. Christian
2014	B	`conf`	Cultural evolution with sparse testimony: when does the cultural ratchet slip? CogSci Andrew Whalen, Luke Maurits, Michael Pacer, Thomas L. Griffiths
2014	B	`conf`	Diagnosing Algebra Understanding via Bayesian Inverse Planning. EDM Anna N. Rafferty, Thomas L. Griffiths
2014	B	`conf`	Empirical Evidence for Markov Chain Monte Carlo in Memory Search. CogSci David Bourgin, Joshua T. Abbott, Thomas L. Griffiths, Kevin A. Smith, Ed Vul
2014	B	`conf`	Is Holism A Problem For Inductive Inference? A Computational Analysis. CogSci Maxwell A. Bertolero, Thomas L. Griffiths
2014	B	`conf`	Moot Point Process Models. CogSci Bradley C. Love, Jana Jarecki, Jerome R. Busemeyer, Niels A. Taatgen, Thomas L. Griffiths, Mirjam Jenny
2014	J	`jnl`	One and Done? Optimal Decisions From Very Few Samples. Cogn. Sci. Edward Vul, Noah D. Goodman, Thomas L. Griffiths, Joshua B. Tenenbaum
2014	B	`conf`	The Telephone Game: Exploring Inductive Biases In Naturalistic Language Use. CogSci Stephan C. Meylan, Brett Goldstein, Anna N. Rafferty, Thomas L. Griffiths
2014	B	`conf`	The high availability of extreme events serves resource-rational decision-making. CogSci Falk Lieder, Ming Hsu, Thomas L. Griffiths
2014	J	`jnl`	Tracing the roots of syntax with Bayesian phylogenetics. Proc. Natl. Acad. Sci. USA Luke Maurits, Thomas L. Griffiths
2014	B	`conf`	What to simulate? Inferring the right direction for mental rotation. CogSci Jessica B. Hamrick, Thomas L. Griffiths
2013	B	`conf`	Approximating Bayesian inference with a sparse distributed memory system. CogSci Joshua T. Abbott, Jessica B. Hamrick, Thomas L. Griffiths
2013	J	`jnl`	Automated reconstruction of ancient languages using probabilistic models of sound change. Proc. Natl. Acad. Sci. USA Alexandre Bouchard-Côté, David Hall, Thomas L. Griffiths, Dan Klein
2013	A	`conf`	Evaluating computational models of explanation using human judgments. UAI Michael Pacer, Joseph Jay Williams, Xi Chen, Tania Lombrozo, Thomas L. Griffiths
2013	J	`jnl`	Evaluating computational models of explanation using human judgments. CoRR Michael Pacer, Joseph Jay Williams, Xi Chen, Tania Lombrozo, Thomas L. Griffiths
2013	B	`conf`	How do you know that? Sensitivity to statistical dependency in social learning. CogSci Andrew Whalen, Daphna Buchsbaum, Thomas L. Griffiths
2013	B	`conf`	Inferring mass in complex physical scenes via probabilistic simulation. CogSci Jessica B. Hamrick, Peter W. Battaglia, Thomas L. Griffiths, Joshua B. Tenenbaum
2013	J	`jnl`	The Effects of Cultural Transmission Are Modulated by the Amount of Information Transmitted. Cogn. Sci. Thomas L. Griffiths, Stephan Lewandowsky, Michael L. Kalish
2013	—	`conf`	Visual Concept Learning: Combining Machine Vision and Bayesian Generalization on Concept Hierarchies. NIPS Yangqing Jia, Joshua T. Abbott, Joseph L. Austerweil, Thomas L. Griffiths, Trevor Darrell
2013	B	`conf`	When does the majority rule? Preschoolers' trust in majority informants varies by task domain. CogSci Jane C. Hu, Daphna Buchsbaum, Thomas L. Griffiths, Fei Xu
2012	—	`conf`	"Burn-in, bias, and the rationality of anchoring". NIPS Falk Lieder, Thomas L. Griffiths, Noah D. Goodman
2012	B	`conf`	A Bayesian Model of Rule Induction in Raven's Progressive Matrices. CogSci Daniel R. Little, Stephan Lewandowsky, Thomas L. Griffiths
2012	J	`jnl`	A Non-Parametric Bayesian Method for Inferring Hidden Causes. CoRR Frank D. Wood, Thomas L. Griffiths, Zoubin Ghahramani
2012	B	`conf`	Comparing the inductive biases of simple neural networks and Bayesian models. CogSci Thomas L. Griffiths, Joseph L. Austerweil, Vincent G. Berthiaume
2012	B	`conf`	Connecting input filtering and selection in language evolution. CogSci Luke Maurits, Thomas L. Griffiths
2012	B	`conf`	Constructing a hypothesis space from the Web for large-scale Bayesian word learning. CogSci Joshua T. Abbott, Joseph L. Austerweil, Thomas L. Griffiths
2012	B	`conf`	Determining people's expectations about the form of causal relationships. CogSci Saiwing Yeung, Christopher G. Lucas, Thomas L. Griffiths
2012	B	`conf`	Do I know that you know what you know? Modeling testimony in causal inference. CogSci Daphna Buchsbaum, Sophie Bridgers, Andrew Whalen, Elizabeth Seiver, Thomas L. Griffiths, Alison Gopnik
2012	B	`conf`	Elements of a rational framework for continuous-time causal induction. CogSci Michael Pacer, Thomas L. Griffiths
2012	—	`conf`	Human memory search as a random walk in a semantic network. NIPS Joshua T. Abbott, Joseph L. Austerweil, Thomas L. Griffiths
2012	B	`conf`	Identifying representations of categories of discrete items using Markov chain Monte Carlo with People. CogSci Anne S. Hsu, Jay B. Martin, Adam N. Sanborn, Thomas L. Griffiths
2012	B	`conf`	Inferring learners' knowledge from observed actions. EDM Anna N. Rafferty, Michelle M. LaMar, Thomas L. Griffiths
2012	B	`conf`	Look-Ahead Monte Carlo with People. CogSci Charles Blundell, Adam Sanborn, Thomas L. Griffiths
2012	B	`conf`	Optimally Designing Games for Cognitive Science Research. CogSci Anna N. Rafferty, Matei Zaharia, Thomas L. Griffiths
2012	B	`conf`	Predicting focal colors with a rational model of representativeness. CogSci Joshua T. Abbott, Terry Regier, Thomas L. Griffiths
2012	J	`jnl`	Structured Priors for Structure Learning. CoRR Vikash Mansinghka, Charles Kemp, Thomas L. Griffiths, Joshua B. Tenenbaum
2012	J	`jnl`	Testing the Efficiency of Markov Chain Monte Carlo With People Using Facial Affect Categories. Cogn. Sci. Jay B. Martin, Thomas L. Griffiths, Adam Sanborn
2012	J	`jnl`	The Author-Topic Model for Authors and Documents CoRR Michal Rosen-Zvi, Thomas L. Griffiths, Mark Steyvers, Padhraic Smyth
2012	J	`jnl`	The Phylogenetic Indian Buffet Process: A Non-Exchangeable Nonparametric Prior for Latent Features CoRR Kurt T. Miller, Thomas L. Griffiths, Michael I. Jordan
2012	B	`conf`	Thirty years of Marr's Vision: Levels of Analysis in Cognitive Science. CogSci Chris Eliasmith, Thomas L. Griffiths, Valerie Gray Hardcastle, Bradley C. Love, William Bechtel, Richard P. Cooper, David Peebles
2011	B	`conf`	A Bayesian model of navigation in squirrels. CogSci Anna Waisman, Christopher G. Lucas, Thomas L. Griffiths, Lucia Jacobs
2011	A*	`conf`	A Nonparametric Bayesian Model of Multi-Level Category Learning. AAAI Kevin Robert Canini, Thomas L. Griffiths
2011	B	`conf`	A Simple Sequential Algorithm for Approximating Bayesian Inference. CogSci Elizabeth Bonawitz, Stephanie Denison, Annie Chen, Alison Gopnik, Thomas L. Griffiths
2011	—	`conf`	A rational model of causal inference with continuous causes. NIPS Michael Pacer, Thomas L. Griffiths
2011	—	`conf`	An ideal observer model for identifying the reference frame of objects. NIPS Joseph L. Austerweil, Abram L. Friesen, Thomas L. Griffiths
2011	J	`jnl`	Bayes and Blickets: Effects of Knowledge on Causal Induction in Children and Adults. Cogn. Sci. Thomas L. Griffiths, David M. Sobel, Joshua B. Tenenbaum, Alison Gopnik
2011	B	`conf`	Discovering Inductive Biases in Categorization through Iterated Learning. CogSci Kevin Robert Canini, Thomas L. Griffiths, Wolf Vanpaemel, Michael L. Kalish
2011	B	`conf`	Estimating human priors on causal strength. CogSci Saiwing Yeung, Thomas L. Griffiths
2011	—	`conf`	Exploring the Relationship Between Learnability and Linguistic Universals. CMCL@ACL Anna N. Rafferty, Thomas L. Griffiths, Marc Ettlinger
2011	B	`conf`	Exploring the influence of particle filter parameters on order effects in causal learning. CogSci Joshua T. Abbott, Thomas L. Griffiths
2011	A	`conf`	Faster Teaching by POMDP Planning. AIED Anna N. Rafferty, Emma Brunskill, Thomas L. Griffiths, Patrick Shafto
2011	B	`conf`	From preferences to choices and back again: evidence for human inconsistency and its implications. CogSci Christopher G. Lucas, Charles Kemp, Thomas L. Griffiths
2011	B	`conf`	Grow your own representations: Computational constructivism. CogSci Joseph L. Austerweil, Thomas L. Griffiths, Todd M. Gureckis, Robert L. Goldstone, Kevin Robert Canini, Matt Jones
2011	J	`jnl`	Producing Power-Law Distributions and Damping Word Frequencies with Two-Stage Language Models. J. Mach. Learn. Res. Sharon Goldwater, Thomas L. Griffiths, Mark Johnson
2011	J	`jnl`	Seeking Confirmation Is Rational for Deterministic Hypotheses. Cogn. Sci. Joseph L. Austerweil, Thomas L. Griffiths
2011	B	`conf`	Segmenting and Recognizing Human Action using Low-level Video Features. CogSci Daphna Buchsbaum, Kevin Robert Canini, Thomas L. Griffiths
2011	—	`conf`	Testing a Bayesian Measure of Representativeness Using a Large Image Database. NIPS Joshua T. Abbott, Katherine A. Heller, Zoubin Ghahramani, Thomas L. Griffiths
2011	J	`jnl`	The Indian Buffet Process: An Introduction and Review. J. Mach. Learn. Res. Thomas L. Griffiths, Zoubin Ghahramani
2011	B	`conf`	Young Toddlers' Understanding of Graded Preferences. CogSci Jane C. Hu, Christopher G. Lucas, Thomas L. Griffiths, Fei Xu
2010	J	`jnl`	Learning author-topic models from text corpora. ACM Trans. Inf. Syst. Michal Rosen-Zvi, Chaitanya Chemudugunta, Thomas L. Griffiths, Padhraic Smyth, Mark Steyvers
2010	—	`conf`	Learning invariant features using the Transformed Indian Buffet Process. NIPS Joseph L. Austerweil, Thomas L. Griffiths
2010	J	`jnl`	Learning the Form of Causal Relationships Using Hierarchical Bayesian Models. Cogn. Sci. Christopher G. Lucas, Thomas L. Griffiths
2010	A*	`conf`	Modeling Transfer Learning in Human Categorization with the Hierarchical Dirichlet Process. ICML Kevin Robert Canini, Mikhail M. Shashkov, Thomas L. Griffiths
2010	J	`jnl`	The nested chinese restaurant process and bayesian nonparametric inference of topic hierarchies. J. ACM David M. Blei, Thomas L. Griffiths, Michael I. Jordan
2009	A	`conf`	Connecting human and machine learning via probabilistic models of cognition. INTERSPEECH Thomas L. Griffiths
2009	—	`conf`	Differential Use of Implicit Negative Evidence in Generative and Discriminative Language Learning. NIPS Anne S. Hsu, Thomas L. Griffiths
2009	—	`conf`	Improved Reconstruction of Protolanguage Word Forms. HLT-NAACL Alexandre Bouchard-Côté, Thomas L. Griffiths, Dan Klein
2009	—	`conf`	Neural Implementation of Hierarchical Bayesian Inference by Importance Sampling. NIPS Lei Shi, Thomas L. Griffiths
2009	—	`conf`	Nonparametric Latent Feature Models for Link Prediction. NIPS Kurt T. Miller, Thomas L. Griffiths, Michael I. Jordan
2009	A	`conf`	Online Inference of Topics with Latent Dirichlet Allocation. AISTATS Kevin Robert Canini, Lei Shi, Thomas L. Griffiths
2009	J	`jnl`	The Wisdom of Individuals: Exploring People's Knowledge About Everyday Events Using Iterated Learning. Cogn. Sci. Stephan Lewandowsky, Thomas L. Griffiths, Michael L. Kalish
2008	J	`jnl`	A Probabilistic Model of Meetings That Combines Words and Discourse Features. IEEE Trans. Speech Audio Process. Mike Dowman, Virginia Savova, Thomas L. Griffiths, Konrad P. Körding, Joshua B. Tenenbaum, Matthew Purver
2008	J	`jnl`	A Rational Analysis of Rule-Based Concept Learning. Cogn. Sci. Noah D. Goodman, Joshua B. Tenenbaum, Jacob Feldman, Thomas L. Griffiths
2008	—	`conf`	A rational model of preference learning and choice prediction by children. NIPS Christopher G. Lucas, Thomas L. Griffiths, Fei Xu, Christine Fawcett
2008	—	`conf`	Analyzing human feature learning as nonparametric Bayesian inference. NIPS Joseph L. Austerweil, Thomas L. Griffiths
2008	—	`conf`	How memory biases affect information transmission: A rational analysis of serial reproduction. NIPS Jing Xu, Thomas L. Griffiths
2008	J	`jnl`	Latent Features in Similarity Judgments: A Nonparametric Bayesian Approach. Neural Comput. Daniel J. Navarro, Thomas L. Griffiths
2008	—	`conf`	Modeling human function learning with Gaussian processes. NIPS Thomas L. Griffiths, Christopher G. Lucas, Joseph Jay Williams, Michael L. Kalish
2008	—	`conf`	Modeling the effects of memory on human online sentence processing with particle filters. NIPS Roger Levy, Florencia Reali, Thomas L. Griffiths
2008	A	`conf`	The Phylogenetic Indian Buffet Process: A Non-Exchangeable Nonparametric Prior for Latent Features. UAI Kurt T. Miller, Thomas L. Griffiths, Michael I. Jordan
2008	J	`jnl`	Using Category Structures to Test Iterated Learning as a Method for Identifying Inductive Biases. Cogn. Sci. Thomas L. Griffiths, Brian R. Christian, Michael L. Kalish
2007	—	`conf`	A Probabilistic Approach to Language Change. NIPS Alexandre Bouchard-Côté, Percy Liang, Thomas L. Griffiths, Dan Klein
2007	A*	`conf`	A fully Bayesian approach to unsupervised part-of-speech tagging. ACL Sharon Goldwater, Thomas L. Griffiths
2007	—	`conf`	Bayesian Inference for PCFGs via Markov Chain Monte Carlo. HLT-NAACL Mark Johnson, Thomas L. Griffiths, Sharon Goldwater
2007	J	`jnl`	Language Evolution by Iterated Learning With Bayesian Agents. Cogn. Sci. Thomas L. Griffiths, Michael L. Kalish
2007	—	`conf`	Markov Chain Monte Carlo with People. NIPS Adam Sanborn, Thomas L. Griffiths
2007	J	`jnl`	Parametric Embedding for Class Visualization. Neural Comput. Tomoharu Iwata, Kazumi Saito, Naonori Ueda, Sean Stromsten, Thomas L. Griffiths, Joshua B. Tenenbaum
2006	A	`conf`	A Non-Parametric Bayesian Method for Inferring Hidden Causes. UAI Frank D. Wood, Thomas L. Griffiths, Zoubin Ghahramani
2006	—	`conf`	A Nonparametric Bayesian Method for Inferring Features From Similarity Judgments. NIPS Daniel J. Navarro, Thomas L. Griffiths
2006	—	`conf`	Adaptor Grammars: A Framework for Specifying Compositional Nonparametric Bayesian Models. NIPS Mark Johnson, Thomas L. Griffiths, Sharon Goldwater
2006	A*	`conf`	Contextual Dependencies in Unsupervised Word Segmentation. ACL Sharon Goldwater, Thomas L. Griffiths, Mark Johnson
2006	A*	`conf`	Learning Systems of Concepts with an Infinite Relational Model. AAAI Charles Kemp, Joshua B. Tenenbaum, Thomas L. Griffiths, Takeshi Yamada, Naonori Ueda
2006	—	`conf`	Particle Filtering for Nonparametric Bayesian Matrix Factorization. NIPS Frank D. Wood, Thomas L. Griffiths
2006	A	`conf`	Structured Priors for Structure Learning. UAI Vikash K. Mansinghka, Charles Kemp, Thomas L. Griffiths, Joshua B. Tenenbaum
2006	A*	`conf`	Unsupervised Topic Modelling for Multi-Party Spoken Discourse. ACL Matthew Purver, Konrad P. Körding, Thomas L. Griffiths, Joshua B. Tenenbaum
2005	—	`conf`	Infinite latent feature models and the Indian buffet process. NIPS Thomas L. Griffiths, Zoubin Ghahramani
2005	—	`conf`	Interpolating between types and tokens by estimating power-law generators. NIPS Sharon Goldwater, Thomas L. Griffiths, Mark Johnson
2004	—	`conf`	Integrating Topics and Syntax. NIPS Thomas L. Griffiths, Mark Steyvers, David M. Blei, Joshua B. Tenenbaum
2004	—	`conf`	Parametric Embedding for Class Visualization. NIPS Tomoharu Iwata, Kazumi Saito, Naonori Ueda, Sean Stromsten, Thomas L. Griffiths, Joshua B. Tenenbaum
2004	A	`conf`	The Author-Topic Model for Authors and Documents. UAI Michal Rosen-Zvi, Thomas L. Griffiths, Mark Steyvers, Padhraic Smyth
2003	—	`conf`	From Algorithmic to Subjective Randomness. NIPS Thomas L. Griffiths, Joshua B. Tenenbaum
2003	—	`conf`	Hierarchical Topic Models and the Nested Chinese Restaurant Process. NIPS David M. Blei, Thomas L. Griffiths, Michael I. Jordan, Joshua B. Tenenbaum
2003	—	`conf`	Semi-Supervised Learning with Trees. NIPS Charles Kemp, Thomas L. Griffiths, Sean Stromsten, Joshua B. Tenenbaum
2002	—	`conf`	Dynamical Causal Learning. NIPS David Danks, Thomas L. Griffiths, Joshua B. Tenenbaum
2002	—	`conf`	Prediction and Semantic Association. NIPS Thomas L. Griffiths, Mark Steyvers
2002	—	`conf`	Theory-Based Causal Inference. NIPS Joshua B. Tenenbaum, Thomas L. Griffiths
2001	—	`conf`	Using Vocabulary Knowledge in Bayesian Multinomial Estimation. NIPS Thomas L. Griffiths, Joshua B. Tenenbaum
2000	—	`conf`	Structure Learning in Human Causal Induction. NIPS Joshua B. Tenenbaum, Thomas L. Griffiths