Griffiths, Thomas L.

103 publications

NeurIPS 2025 Are Large Language Models Sensitive to the Motives Behind Communication? Addison J. Wu, Ryan Liu, Kerem Oktar, Theodore Sumers, Thomas L. Griffiths

NeurIPS 2025 Causal Head Gating: A Framework for Interpreting Roles of Attention Heads in Transformers Andrew Joohun Nam, Henry Conklin, Yukang Yang, Thomas L. Griffiths, Jonathan D. Cohen, Sarah-Jane Leslie

ICML 2025 Conformal Prediction as Bayesian Quadrature Jake C. Snell, Thomas L. Griffiths

TMLR 2025 Getting Aligned on Representational Alignment Ilia Sucholutsky, Lukas Muttenthaler, Adrian Weller, Andi Peng, Andreea Bobu, Been Kim, Bradley C. Love, Christopher J Cueva, Erin Grant, Iris Groen, Jascha Achterberg, Joshua B. Tenenbaum, Katherine M. Collins, Katherine 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 O'Connell, Thomas Unterthiner, Andrew Kyle Lampinen, Klaus Robert Muller, Mariya Toneva, Thomas L. Griffiths

UAI 2025 Hindsight Merging: Diverse Data Generation with Language Models Veniamin Veselovsky, Benedikt Stroebl, Gianluca Bencomo, Dilip Arumugam, Lisa Schut, Arvind Narayanan, Thomas L. Griffiths

ICLR 2025 Language Models Trained to Do Arithmetic Predict Human Risky and Intertemporal Choice Jian-Qiao Zhu, Haijiang Yan, Thomas L. Griffiths

ICLR 2025 Large Language Models Assume People Are More Rational than We Really Are Ryan Liu, Jiayi Geng, Joshua Peterson, Ilia Sucholutsky, Thomas L. Griffiths

ICML 2025 Mind Your Step (by Step): Chain-of-Thought Can Reduce Performance on Tasks Where Thinking Makes Humans Worse Ryan Liu, Jiayi Geng, Addison J. Wu, Ilia Sucholutsky, Tania Lombrozo, Thomas L. Griffiths

NeurIPS 2025 Partner Modelling Emerges in Recurrent Agents (But Only When It Matters) Ruaridh Mon-Williams, Max Taylor-Davies, Elizabeth Mieczkowski, Natalia Vélez, Neil R Bramley, Yanwei Wang, Thomas L. Griffiths, Christopher G. Lucas

ICLRW 2025 Representational Alignment Supports Effective Teaching Ilia Sucholutsky, Katherine M. Collins, Maya Malaviya, Nori Jacoby, Weiyang Liu, Theodore Sumers, Michalis Korakakis, Umang Bhatt, Mark K Ho, Joshua B. Tenenbaum, Bradley C. Love, Zachary Pardos, Adrian Weller, Thomas L. Griffiths

ICLRW 2025 Understanding Task Representations in Neural Networks via Bayesian Ablation Andrew Joohun Nam, Declan Iain Campbell, Thomas L. Griffiths, Jonathan D. Cohen, Sarah-Jane Leslie

TMLR 2025 What Should Embeddings Embed? Autoregressive Models Represent Latent Generating Distributions Liyi Zhang, Michael Y. Li, R. Thomas McCoy, Theodore Sumers, Jian-Qiao Zhu, Thomas L. Griffiths

NeurIPS 2024 A Metalearned Neural Circuit for Nonparametric Bayesian Inference Jake C. Snell, Gianluca M. Bencomo, Thomas L. Griffiths

ICLRW 2024 Can Generative Multimodal Models Count to Ten? Sunayana Rane, Alexander Ku, Jason Michael Baldridge, Ian Tenney, Thomas L. Griffiths, Been Kim

TMLR 2024 Cognitive Architectures for Language Agents Theodore Sumers, Shunyu Yao, Karthik R Narasimhan, Thomas L. Griffiths

ICLRW 2024 Comparing Abstraction in Humans and Large Language Models Using Multimodal Serial Reproduction Sreejan Kumar, Raja Marjieh, Byron Zhang, Declan Iain Campbell, Michael Y. Hu, Umang Bhatt, Brenden Lake, Thomas L. Griffiths

NeurIPSW 2024 Early Exiting in Deep Neural Networks via Dirichlet-Based Uncertainty Quantification Feng Xia, Jake Snell, Thomas L. Griffiths

NeurIPSW 2024 Embodied LLM Agents Learn to Cooperate in Organized Teams Xudong Guo, Kaixuan Huang, Jiale Liu, Wenhui Fan, Natalia Vélez, Qingyun Wu, Huazheng Wang, Thomas L. Griffiths, Mengdi Wang

NeurIPSW 2024 Higher Uncertainty Leads to Less Exploration in a Combinatorial Discovery Game Bonan Zhao, Natalia Vélez, Thomas L. Griffiths

ICML 2024 How Do Large Language Models Navigate Conflicts Between Honesty and Helpfulness? Ryan Liu, Theodore Sumers, Ishita Dasgupta, Thomas L. Griffiths

ICLRW 2024 Human-like Geometric Abstraction in Large Pre-Trained Neural Networks Declan Iain Campbell, Sreejan Kumar, Tyler Giallanza, Jonathan D. Cohen, Thomas L. Griffiths

ICLR 2024 Implicit Maximum a Posteriori Filtering via Adaptive Optimization Gianluca Bencomo, Jake Snell, Thomas L. Griffiths

NeurIPSW 2024 Investigating Same-Different Concept Understanding in Generative Multimodal Models Sunayana Rane, Declan Iain Campbell, Thomas L. Griffiths

NeurIPS 2024 Learning Human-like Representations to Enable Learning Human Values Andrea H. Wynn, Ilia Sucholutsky, Thomas L. Griffiths

ICLR 2024 Learning with Language-Guided State Abstractions Andi Peng, Ilia Sucholutsky, Belinda Z. Li, Theodore Sumers, Thomas L. Griffiths, Jacob Andreas, Julie Shah

NeurIPSW 2024 Measuring Implicit Bias in Explicitly Unbiased Large Language Models Xuechunzi Bai, Angelina Wang, Ilia Sucholutsky, Thomas L. Griffiths

NeurIPSW 2024 Modeling Cognitive Strategies in Teaching Sevan K Harootonian, Yael Niv, Thomas L. Griffiths, Mark K Ho

NeurIPSW 2024 Predicting Human Decisions with Behavioral Theories and Machine Learning Ori Plonsky, Reut Apel, Eyal Ert, Moshe Tennenholtz, David Bourgin, Joshua Peterson, Daniel Reichman, Thomas L. Griffiths, Stuart Russell, Evan Carter, James F. Cavanagh, Ido Erev

ICLRW 2024 Preference-Conditioned Language-Guided Abstraction Andi Peng, Andreea Bobu, Belinda Z. Li, Theodore Sumers, Ilia Sucholutsky, Nishanth Kumar, Thomas L. Griffiths, Julie Shah

NeurIPSW 2024 RLHS: Mitigating Misalignment in RLHF with Hindsight Simulation Kaiqu Liang, Haimin Hu, Ryan Liu, Thomas L. Griffiths, Jaime Fernández Fisac

NeurIPSW 2024 Rational Metareasoning for Large Language Models C. Nicolò De Sabbata, Theodore Sumers, Thomas L. Griffiths

ICMLW 2024 Thinking Out-of-the-Box: A Comparative Investigation of Human and LLMs in Creative Problem-Solving Yufei Tian, Abhilasha Ravichander, Lianhui Qin, Ronan Le Bras, Raja Marjieh, Nanyun Peng, Yejin Choi, Thomas L. Griffiths, Faeze Brahman

NeurIPS 2024 Understanding the Limits of Vision Language Models Through the Lens of the Binding Problem 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 Webb

ICML 2023 Analyzing Diffusion as Serial Reproduction Raja Marjieh, Ilia Sucholutsky, Thomas A Langlois, Nori Jacoby, Thomas L. Griffiths

UAI 2023 Gaussian Process Surrogate Models for Neural Networks Michael Y. Li, Erin Grant, Thomas L. Griffiths

ICLR 2023 Hierarchical Abstraction for Combinatorial Generalization in Object Rearrangement Michael Chang, Alyssa Li Dayan, Franziska Meier, Thomas L. Griffiths, Sergey Levine, Amy Zhang

NeurIPSW 2023 Inverting Cognitive Models with Machine Learning to Infer Preferences from Fixations Evan Russek, Frederick Callaway, Thomas L. Griffiths

UAI 2023 On the Informativeness of Supervision Signals Ilia Sucholutsky, Ruairidh M. Battleday, Katherine M. Collins, Raja Marjieh, Joshua Peterson, Pulkit Singh, Umang Bhatt, Nori Jacoby, Adrian Weller, Thomas L. Griffiths

ICLR 2023 Words Are All You Need? Language as an Approximation for Human Similarity Judgments Raja Marjieh, Pol Van Rijn, Ilia Sucholutsky, Theodore Sumers, Harin Lee, Thomas L. Griffiths, Nori Jacoby

NeurIPSW 2022 Hierarchical Abstraction for Combinatorial Generalization in Object Rearrangement Michael Chang, Alyssa Li Dayan, Franziska Meier, Thomas L. Griffiths, Sergey Levine, Amy Zhang

NeurIPSW 2022 How to Talk so AI Will Learn: Instructions, Descriptions, and Pragmatics Theodore Sumers, Robert D. Hawkins, Mark K Ho, Thomas L. Griffiths, Dylan Hadfield-Menell

ICLRW 2022 Object Representations as Equilibria: Training Iterative Inference Algorithms with Implicit Differentiation Michael Chang, Thomas L. Griffiths, Sergey Levine

ICLRW 2022 Object Representations as Fixed Points: Training Iterative Inference Algorithms with Implicit Differentiation Michael Chang, Thomas L. Griffiths, Sergey Levine

ICLRW 2022 Object-Centric Learning as Nested Optimization Michael Chang, Sergey Levine, Thomas L. Griffiths

NeurIPSW 2022 On the Informativeness of Supervision Signals Ilia Sucholutsky, Raja Marjieh, Thomas L. Griffiths

ICMLW 2022 Predicting Human Similarity Judgments Using Large Language Models Raja Marjieh, Ilia Sucholutsky, Theodore Sumers, Nori Jacoby, Thomas L. Griffiths

NeurIPSW 2021 Exploring the Structure of Human Adjective Representations Karan Grewal, Joshua Peterson, Bill D Thompson, Thomas L. Griffiths

AAAI 2021 Learning Rewards from Linguistic Feedback Theodore R. Sumers, Mark K. Ho, Robert X. D. Hawkins, Karthik Narasimhan, Thomas L. Griffiths

NeurIPSW 2021 Meta-Learning Inductive Biases of Learning Systems with Gaussian Processes Michael Y. Li, Erin Grant, Thomas L. Griffiths

ICLRW 2021 Modularity in Reinforcement Learning via Algorithmic Independence in Credit Assignment Michael Chang, Sidhant Kaushik, Thomas L. Griffiths, Sergey Levine

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

ICLR 2019 Automatically Composing Representation Transformations as a Means for Generalization Michael Chang, Abhishek Gupta, Sergey Levine, Thomas L. Griffiths

ICML 2019 Cognitive Model Priors for Predicting Human Decisions David D. Bourgin, Joshua C. Peterson, Daniel Reichman, Stuart J. Russell, Thomas L. Griffiths

UAI 2018 Learning to Select Computations Frederick Callaway, Sayan Gul, Paul M. Krueger, Thomas L. Griffiths, Falk Lieder

IJCAI 2017 Adapting Deep Network Features to Capture Psychological Representations: An Abridged Report Joshua C. Peterson, Joshua T. Abbott, Thomas L. Griffiths

AAAI 2017 When Does Bounded-Optimal Metareasoning Favor Few Cognitive Systems? Smitha Milli, Falk Lieder, Thomas L. Griffiths

UAI 2013 Evaluating Computational Models of Explanation Using Human Judgments Michael Pacer, Joseph Jay Williams, Xi Chen, Tania Lombrozo, Thomas L. Griffiths

NeurIPS 2012 Human Memory Search as a Random Walk in a Semantic Network Joseph L. Austerweil, Joshua T. Abbott, Thomas L. Griffiths

AAAI 2011 A Nonparametric Bayesian Model of Multi-Level Category Learning Kevin Robert Canini, Thomas L. Griffiths

NeurIPS 2011 A Rational Model of Causal Inference with Continuous Causes Thomas L. Griffiths, Michael James

NeurIPS 2011 An Ideal Observer Model for Identifying the Reference Frame of Objects Joseph L. Austerweil, Abram L. Friesen, Thomas L. Griffiths

JMLR 2011 Producing Power-Law Distributions and Damping Word Frequencies with Two-Stage Language Models Sharon Goldwater, Thomas L. Griffiths, Mark Johnson

NeurIPS 2011 Testing a Bayesian Measure of Representativeness Using a Large Image Database Joshua T. Abbott, Katherine A. Heller, Zoubin Ghahramani, Thomas L. Griffiths

JMLR 2011 The Indian Buffet Process: An Introduction and Review Thomas L. Griffiths, Zoubin Ghahramani

NeurIPS 2010 Learning Invariant Features Using the Transformed Indian Buffet Process Joseph L. Austerweil, Thomas L. Griffiths

ICML 2010 Modeling Transfer Learning in Human Categorization with the Hierarchical Dirichlet Process Kevin Robert Canini, Mikhail M. Shashkov, Thomas L. Griffiths

NeurIPS 2009 Differential Use of Implicit Negative Evidence in Generative and Discriminative Language Learning Anne Hsu, Thomas L. Griffiths

NeurIPS 2009 Neural Implementation of Hierarchical Bayesian Inference by Importance Sampling Lei Shi, Thomas L. Griffiths

NeurIPS 2009 Nonparametric Latent Feature Models for Link Prediction Kurt Miller, Michael I. Jordan, Thomas L. Griffiths

NeurIPS 2008 A Rational Model of Preference Learning and Choice Prediction by Children Christopher G. Lucas, Thomas L. Griffiths, Fei Xu, Christine Fawcett

NeurIPS 2008 Analyzing Human Feature Learning as Nonparametric Bayesian Inference Thomas L. Griffiths, Joseph L. Austerweil

NeurIPS 2008 How Memory Biases Affect Information Transmission: A Rational Analysis of Serial Reproduction Jing Xu, Thomas L. Griffiths

NeurIPS 2008 Modeling Human Function Learning with Gaussian Processes Thomas L. Griffiths, Chris Lucas, Joseph Williams, Michael L. Kalish

NeurIPS 2008 Modeling the Effects of Memory on Human Online Sentence Processing with Particle Filters Roger P. Levy, Florencia Reali, Thomas L. Griffiths

UAI 2008 The Phylogenetic Indian Buffet Process: A Non-Exchangeable Nonparametric Prior for Latent Features Kurt T. Miller, Thomas L. Griffiths, Michael I. Jordan

NeurIPS 2007 A Probabilistic Approach to Language Change Alexandre Bouchard-côté, Percy Liang, Dan Klein, Thomas L. Griffiths

NeurIPS 2007 Markov Chain Monte Carlo with People Adam Sanborn, Thomas L. Griffiths

UAI 2006 A Non-Parametric Bayesian Method for Inferring Hidden Causes Frank D. Wood, Thomas L. Griffiths, Zoubin Ghahramani

NeurIPS 2006 A Nonparametric Bayesian Method for Inferring Features from Similarity Judgments Daniel J. Navarro, Thomas L. Griffiths

NeurIPS 2006 Adaptor Grammars: A Framework for Specifying Compositional Nonparametric Bayesian Models Mark Johnson, Thomas L. Griffiths, Sharon Goldwater

AAAI 2006 Learning Systems of Concepts with an Infinite Relational Model Charles Kemp, Joshua B. Tenenbaum, Thomas L. Griffiths, Takeshi Yamada, Naonori Ueda

NeurIPS 2006 Particle Filtering for Nonparametric Bayesian Matrix Factorization Frank Wood, Thomas L. Griffiths

UAI 2006 Structured Priors for Structure Learning Vikash K. Mansinghka, Charles Kemp, Thomas L. Griffiths, Joshua B. Tenenbaum

NeurIPS 2005 Infinite Latent Feature Models and the Indian Buffet Process Zoubin Ghahramani, Thomas L. Griffiths

NeurIPS 2005 Interpolating Between Types and Tokens by Estimating Power-Law Generators Sharon Goldwater, Mark Johnson, Thomas L. Griffiths

NeurIPS 2004 Integrating Topics and Syntax Thomas L. Griffiths, Mark Steyvers, David M. Blei, Joshua B. Tenenbaum

NeurIPS 2004 Parametric Embedding for Class Visualization Tomoharu Iwata, Kazumi Saito, Naonori Ueda, Sean Stromsten, Thomas L. Griffiths, Joshua B. Tenenbaum

UAI 2004 The Author-Topic Model for Authors and Documents Michal Rosen-Zvi, Thomas L. Griffiths, Mark Steyvers, Padhraic Smyth

NeurIPS 2003 From Algorithmic to Subjective Randomness Thomas L. Griffiths, Joshua B. Tenenbaum

NeurIPS 2003 Hierarchical Topic Models and the Nested Chinese Restaurant Process Thomas L. Griffiths, Michael I. Jordan, Joshua B. Tenenbaum, David M. Blei

NeurIPS 2003 Semi-Supervised Learning with Trees Charles Kemp, Thomas L. Griffiths, Sean Stromsten, Joshua B. Tenenbaum

NeurIPS 2002 Dynamical Causal Learning David Danks, Thomas L. Griffiths, Joshua B. Tenenbaum

NeurIPS 2002 Prediction and Semantic Association Thomas L. Griffiths, Mark Steyvers

NeurIPS 2002 Theory-Based Causal Inference Joshua B. Tenenbaum, Thomas L. Griffiths

NeurIPS 2001 Using Vocabulary Knowledge in Bayesian Multinomial Estimation Thomas L. Griffiths, Joshua B. Tenenbaum

NeurIPS 2000 Structure Learning in Human Causal Induction Joshua B. Tenenbaum, Thomas L. Griffiths