Thomas L. Griffiths

University of California, Berkeley, Department of Psychology

	2012
j16		Jay B. Martin, Thomas L. Griffiths, Adam Sanborn: Testing the Efficiency of Markov Chain Monte Carlo With People Using Facial Affect Categories. Cognitive Science 36(1): 150-162 (2012)
c49		Anna N. Rafferty, Michelle Lamar, Thomas L. Griffiths: Inferring learners' knowledge from observed actions. EDM 2012: 226-227
c48		Falk Lieder, Thomas L. Griffiths, Noah D. Goodman: "Burn-in, bias, and the rationality of anchoring". NIPS 2012: 2699-2707
c47		Joshua T. Abbott, Joseph L. Austerweil, Thomas L. Griffiths: Human memory search as a random walk in a semantic network. NIPS 2012: 3050-3058
i2		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)
i1		Michal Rosen-Zvi, Thomas L. Griffiths, Mark Steyvers, Padhraic Smyth: The Author-Topic Model for Authors and Documents. CoRR abs/1207.4169 (2012)
	2011
j15		Joseph L. Austerweil, Thomas L. Griffiths: Seeking Confirmation Is Rational for Deterministic Hypotheses. Cognitive Science 35(3): 499-526 (2011)
j14		Thomas L. Griffiths, David M. Sobel, Joshua B. Tenenbaum, Alison Gopnik: Bayes and Blickets: Effects of Knowledge on Causal Induction in Children and Adults. Cognitive Science 35(8): 1407-1455 (2011)
j13		Thomas L. Griffiths, Zoubin Ghahramani: The Indian Buffet Process: An Introduction and Review. Journal of Machine Learning Research 12: 1185-1224 (2011)
j12		Sharon Goldwater, Thomas L. Griffiths, Mark Johnson: Producing Power-Law Distributions and Damping Word Frequencies with Two-Stage Language Models. Journal of Machine Learning Research 12: 2335-2382 (2011)
c46		Kevin Robert Canini, Thomas L. Griffiths: A Nonparametric Bayesian Model of Multi-Level Category Learning. AAAI 2011
c45		Anna N. Rafferty, Emma Brunskill, Thomas L. Griffiths, Patrick Shafto: Faster Teaching by POMDP Planning. AIED 2011: 280-287
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
j11		Christopher G. Lucas, Thomas L. Griffiths: Learning the Form of Causal Relationships Using Hierarchical Bayesian Models. Cognitive Science 34(1): 113-147 (2010)
j10		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) (2010)
j9		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) (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
j8		Stephan Lewandowsky, Thomas L. Griffiths, Michael L. Kalish: The Wisdom of Individuals: Exploring People's Knowledge About Everyday Events Using Iterated Learning. Cognitive Science 33(6): 969-998 (2009)
j7		Kevin Robert Canini, Lei Shi, Thomas L. Griffiths: Online Inference of Topics with Latent Dirichlet Allocation. Journal of Machine Learning Research - Proceedings Track 5: 65-72 (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
	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. Cognitive Science 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. Cognitive Science 32(1): 108-154 (2008)
j4		Daniel J. Navarro, Thomas L. Griffiths: Latent Features in Similarity Judgments: A Nonparametric Bayesian Approach. Neural Computation 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 Transactions on Audio, Speech & Language Processing 16(7): 1238-1248 (2008)
c34		Joseph L. Austerweil, Thomas L. Griffiths: Analyzing human feature learning as nonparametric Bayesian inference. NIPS 2008: 97-104
c33		Thomas L. Griffiths, Christopher G. Lucas, Joseph Williams, Michael L. Kalish: Modeling human function learning with Gaussian processes. NIPS 2008: 553-560
c32		Roger P. Levy, Florencia Reali, Thomas L. Griffiths: Modeling the effects of memory on human online sentence processing with particle filters. NIPS 2008: 937-944
c31		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
c30		Jing Xu, Thomas L. Griffiths: How memory biases affect information transmission: A rational analysis of serial reproduction. NIPS 2008: 1809-1816
c29		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
j2		Thomas L. Griffiths, Michael L. Kalish: Language Evolution by Iterated Learning With Bayesian Agents. Cognitive Science 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 Computation 19(9): 2536-2556 (2007)
c28		Sharon Goldwater, Thomas L. Griffiths: A fully Bayesian approach to unsupervised part-of-speech tagging. ACL 2007
c27		Alexandre Bouchard-Côté, Percy Liang, Thomas L. Griffiths, Dan Klein: A Probabilistic Approach to Diachronic Phonology. EMNLP-CoNLL 2007: 887-896
c26		Mark Johnson, Thomas L. Griffiths, Sharon Goldwater: Bayesian Inference for PCFGs via Markov Chain Monte Carlo. HLT-NAACL 2007: 139-146
c25		Alexandre Bouchard-Côté, Percy Liang, Thomas L. Griffiths, Dan Klein: A Probabilistic Approach to Language Change. NIPS 2007
c24		Adam Sanborn, Thomas L. Griffiths: Markov Chain Monte Carlo with People. NIPS 2007
	2006
c23		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
c22		Sharon Goldwater, Thomas L. Griffiths, Mark Johnson: Contextual Dependencies in Unsupervised Word Segmentation. ACL 2006
c21		Matthew Purver, Konrad P. Körding, Thomas L. Griffiths, Joshua B. Tenenbaum: Unsupervised Topic Modelling for Multi-Party Spoken Discourse. ACL 2006
c20		Mark Johnson, Thomas L. Griffiths, Sharon Goldwater: Adaptor Grammars: A Framework for Specifying Compositional Nonparametric Bayesian Models. NIPS 2006: 641-648
c19		Daniel J. Navarro, Thomas L. Griffiths: A Nonparametric Bayesian Method for Inferring Features From Similarity Judgments. NIPS 2006: 1033-1040
c18		Frank Wood, Thomas L. Griffiths: Particle Filtering for Nonparametric Bayesian Matrix Factorization. NIPS 2006: 1513-1520
c17		Vikash K. Mansinghka, Charles Kemp, Thomas L. Griffiths, Joshua B. Tenenbaum: Structured Priors for Structure Learning. UAI 2006
c16		Frank Wood, Thomas L. Griffiths, Zoubin Ghahramani: A Non-Parametric Bayesian Method for Inferring Hidden Causes. UAI 2006
	2005
c15		Mark Everingham, Andrew Zisserman, Christopher K. I. Williams, Luc J. Van Gool, Moray Allan, Christopher M. Bishop, Olivier Chapelle, Navneet Dalal, Thomas Deselaers, Gyuri Dorkó, Stefan Duffner, Jan Eichhorn, Jason D. R. Farquhar, Mario Fritz, Christophe Garcia, Thomas L. Griffiths, Frédéric Jurie, Daniel Keysers, Markus Koskela, Jorma Laaksonen, Diane Larlus, Bastian Leibe, Hongying Meng, Hermann Ney, Bernt Schiele, Cordelia Schmid, Edgar Seemann, John Shawe-Taylor, Amos J. Storkey, Sándor Szedmák, Bill Triggs, Ilkay Ulusoy, Ville Viitaniemi, Jianguo Zhang: The 2005 PASCAL Visual Object Classes Challenge. MLCW 2005: 117-176
c14		Sharon Goldwater, Thomas L. Griffiths, Mark Johnson: Interpolating between types and tokens by estimating power-law generators. NIPS 2005
c13		Thomas L. Griffiths, Zoubin Ghahramani: Infinite latent feature models and the Indian buffet process. NIPS 2005
	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
c10		Tomoharu Iwata, Kazumi Saito, Naonori Ueda, Sean Stromsten, Thomas L. Griffiths, Joshua B. Tenenbaum: Parametric Embedding for Class Visualization. NIPS 2004
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
c7		Thomas L. Griffiths, Joshua B. Tenenbaum: From Algorithmic to Subjective Randomness. NIPS 2003
c6		Charles Kemp, Thomas L. Griffiths, Sean Stromsten, Joshua B. Tenenbaum: Semi-Supervised Learning with Trees. NIPS 2003
	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

Coauthor Index

Colors in the list of coauthors

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