Thomas L. Griffiths

	2012
54		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)
	2011
53		Kevin Robert Canini, Thomas L. Griffiths: A Nonparametric Bayesian Model of Multi-Level Category Learning. AAAI 2011
52		Anna N. Rafferty, Emma Brunskill, Thomas L. Griffiths, Patrick Shafto: Faster Teaching by POMDP Planning. AIED 2011: 280-287
51		Joseph Austerweil, Thomas L. Griffiths: Seeking Confirmation Is Rational for Deterministic Hypotheses. Cognitive Science 35(3): 499-526 (2011)
50		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)
49		Thomas L. Griffiths, Zoubin Ghahramani: The Indian Buffet Process: An Introduction and Review. Journal of Machine Learning Research 12: 1185-1224 (2011)
48		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)
	2010
47		Kevin Robert Canini, Mikhail M. Shashkov, Thomas L. Griffiths: Modeling Transfer Learning in Human Categorization with the Hierarchical Dirichlet Process. ICML 2010: 151-158
46		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)
45		Christopher G. Lucas, Thomas L. Griffiths: Learning the Form of Causal Relationships Using Hierarchical Bayesian Models. Cognitive Science 34(1): 113-147 (2010)
44		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)
	2009
43		Alexandre Bouchard-Côté, Thomas L. Griffiths, Dan Klein: Improved Reconstruction of Protolanguage Word Forms. HLT-NAACL 2009: 65-73
42		Thomas L. Griffiths: Connecting human and machine learning via probabilistic models of cognition. INTERSPEECH 2009: 9-12
41		Kurt T. Miller, Thomas L. Griffiths, Michael I. Jordan: Nonparametric Latent Feature Models for Link Prediction. NIPS 2009: 1276-1284
40		Lei Shi, Thomas L. Griffiths: Neural Implementation of Hierarchical Bayesian Inference by Importance Sampling. NIPS 2009: 1669-1677
39		Anne S. Hsu, Thomas L. Griffiths: Differential Use of Implicit Negative Evidence in Generative and Discriminative Language Learning. NIPS 2009: 754-762
38		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)
37		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)
	2008
36		Jing Xu, Thomas L. Griffiths: How memory biases affect information transmission: A rational analysis of serial reproduction. NIPS 2008: 1809-1816
35		Thomas L. Griffiths, Christopher G. Lucas, Joseph Williams, Michael L. Kalish: Modeling human function learning with Gaussian processes. NIPS 2008: 553-560
34		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
33		Joseph Austerweil, Thomas L. Griffiths: Analyzing human feature learning as nonparametric Bayesian inference. NIPS 2008: 97-104
32		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
31		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
30		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)
29		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)
28		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)
27		Daniel J. Navarro, Thomas L. Griffiths: Latent Features in Similarity Judgments: A Nonparametric Bayesian Approach. Neural Computation 20(11): 2597-2628 (2008)
	2007
26		Alexandre Bouchard-Côté, Percy Liang, Thomas L. Griffiths, Dan Klein: A Probabilistic Approach to Diachronic Phonology. EMNLP-CoNLL 2007: 887-896
25		Mark Johnson, Thomas L. Griffiths, Sharon Goldwater: Bayesian Inference for PCFGs via Markov Chain Monte Carlo. HLT-NAACL 2007: 139-146
24		Alexandre Bouchard-Côté, Percy Liang, Thomas L. Griffiths, Dan Klein: A Probabilistic Approach to Language Change. NIPS 2007
23		Adam Sanborn, Thomas L. Griffiths: Markov Chain Monte Carlo with People. NIPS 2007
22		Thomas L. Griffiths, Michael L. Kalish: Language Evolution by Iterated Learning With Bayesian Agents. Cognitive Science 31(3): 441-480 (2007)
21		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)
	2006
20		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
19		Sharon Goldwater, Thomas L. Griffiths, Mark Johnson: Contextual Dependencies in Unsupervised Word Segmentation. ACL 2006
18		Matthew Purver, Konrad P. Körding, Thomas L. Griffiths, Joshua B. Tenenbaum: Unsupervised Topic Modelling for Multi-Party Spoken Discourse. ACL 2006
17		Daniel J. Navarro, Thomas L. Griffiths: A Nonparametric Bayesian Method for Inferring Features From Similarity Judgments. NIPS 2006: 1033-1040
16		Frank Wood, Thomas L. Griffiths: Particle Filtering for Nonparametric Bayesian Matrix Factorization. NIPS 2006: 1513-1520
15		Mark Johnson, Thomas L. Griffiths, Sharon Goldwater: Adaptor Grammars: A Framework for Specifying Compositional Nonparametric Bayesian Models. NIPS 2006: 641-648
14		Frank Wood, Thomas L. Griffiths, Zoubin Ghahramani: A Non-Parametric Bayesian Method for Inferring Hidden Causes. UAI 2006
13		Vikash K. Mansinghka, Charles Kemp, Thomas L. Griffiths, Joshua B. Tenenbaum: Structured Priors for Structure Learning. UAI 2006
	2004
12		Mark Steyvers, Padhraic Smyth, Michal Rosen-Zvi, Thomas L. Griffiths: Probabilistic author-topic models for information discovery. KDD 2004: 306-315
11		Thomas L. Griffiths, Mark Steyvers, David M. Blei, Joshua B. Tenenbaum: Integrating Topics and Syntax. NIPS 2004
10		Tomoharu Iwata, Kazumi Saito, Naonori Ueda, Sean Stromsten, Thomas L. Griffiths, Joshua B. Tenenbaum: Parametric Embedding for Class Visualization. NIPS 2004
9		Michal Rosen-Zvi, Thomas L. Griffiths, Mark Steyvers, Padhraic Smyth: The Author-Topic Model for Authors and Documents. UAI 2004: 487-494
	2003
8		Thomas L. Griffiths, Joshua B. Tenenbaum: From Algorithmic to Subjective Randomness. NIPS 2003
7		David M. Blei, Thomas L. Griffiths, Michael I. Jordan, Joshua B. Tenenbaum: Hierarchical Topic Models and the Nested Chinese Restaurant Process. NIPS 2003
6		Charles Kemp, Thomas L. Griffiths, Sean Stromsten, Joshua B. Tenenbaum: Semi-Supervised Learning with Trees. NIPS 2003
	2002
5		Thomas L. Griffiths, Mark Steyvers: Prediction and Semantic Association. NIPS 2002: 11-18
4		Joshua B. Tenenbaum, Thomas L. Griffiths: Theory-Based Causal Inference. NIPS 2002: 35-42
3		David Danks, Thomas L. Griffiths, Joshua B. Tenenbaum: Dynamical Causal Learning. NIPS 2002: 67-74
	2001
2		Thomas L. Griffiths, Joshua B. Tenenbaum: Using Vocabulary Knowledge in Bayesian Multinomial Estimation. NIPS 2001: 1385-1392
	2000
1		Joshua B. Tenenbaum, Thomas L. Griffiths: Structure Learning in Human Causal Induction. NIPS 2000: 59-65

Coauthor Index

1	Joseph Austerweil	[33] [51]
2	David M. Blei	[7] [11] [44]
3	Alexandre Bouchard-Côté	[24] [26] [43]
4	Emma Brunskill	[52]
5	Kevin Robert Canini	[37] [47] [53]
6	Chaitanya Chemudugunta	[46]
7	Brian R. Christian	[29]
8	David Danks	[3]
9	Mike Dowman	[28]
10	Christine Fawcett	[32]
11	Jacob Feldman	[30]
12	Zoubin Ghahramani	[14] [49]
13	Sharon Goldwater	[15] [19] [25] [48]
14	Noah D. Goodman	[30]
15	Alison Gopnik	[50]
16	Anne S. Hsu	[39]
17	Tomoharu Iwata	[10] [21]
18	Mark Johnson	[15] [19] [25] [48]
19	Michael I. Jordan	[7] [31] [41] [44]
20	Michael L. Kalish	[22] [29] [35] [38]
21	Charles Kemp	[6] [13] [20]
22	Dan Klein	[24] [26] [43]
23	Konrad P. Körding	[18] [28]
24	Roger P. Levy	[34]
25	Stephan Lewandowsky	[38]
26	Percy Liang	[24] [26]
27	Christopher G. Lucas	[32] [35] [45]
28	Vikash K. Mansinghka	[13]
29	Jay B. Martin	[54]
30	Kurt T. Miller	[31] [41]
31	Daniel J. Navarro	[17] [27]
32	Matthew Purver	[18] [28]
33	Anna N. Rafferty	[52]
34	Florencia Reali	[34]
35	Michal Rosen-Zvi	[9] [12] [46]
36	Kazumi Saito	[10] [21]
37	Adam Sanborn	[23] [54]
38	Virginia Savova	[28]
39	Patrick Shafto	[52]
40	Mikhail M. Shashkov	[47]
41	Lei Shi	[37] [40]
42	Padhraic Smyth	[9] [12] [46]
43	David M. Sobel	[50]
44	Mark Steyvers	[5] [9] [11] [12] [46]
45	Sean Stromsten	[6] [10] [21]
46	Joshua B. Tenenbaum	[1] [2] [3] [4] [6] [7] [8] [10] [11] [13] [18] [20] [21] [28] [30] [50]
47	Naonori Ueda	[10] [20] [21]
48	Joseph Williams	[35]
49	Frank Wood	[14] [16]
50	Fei Xu	[32]
51	Jing Xu	[36]
52	Takeshi Yamada	[20]

Colors in the list of coauthors

Last update Fri Jun 1 15:44:53 2012 CET by the DBLP Team — Data released under the ODC-BY 1.0 license — See also our legal information page