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ORCIDTom Minka
Person information
- affiliation: Microsoft Research
- affiliation: Carnegie Mellon University, Department of Statistics
- affiliation: Massachusetts Institute of Technology, Media Lab
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2020 – today
- 2023
[i7]Cheng Zhang, Stefan Bauer, Paul Bennett, Jiangfeng Gao, Wenbo Gong, Agrin Hilmkil, Joel Jennings, Chao Ma, Tom Minka, Nick Pawlowski, James Vaughan:
Understanding Causality with Large Language Models: Feasibility and Opportunities. CoRR abs/2304.05524 (2023)- 2021
- [c36]John M. Winn, Matteo Venanzi, Tom Minka, Ivan Korostelev, John Guiver, Elena Pochernina, Pavel Mishkov, Alex Spengler, Denise J. Wilkins, Siân E. Lindley, Richard Banks, Sam Webster, Yordan Zaykov:
Enterprise Alexandria: Online High-Precision Enterprise Knowledge Base Construction with Typed Entities. AKBC 2021
2010 – 2019
- 2014
- [c35]Chris J. Maddison, Daniel Tarlow, Tom Minka:
A* Sampling. NIPS 2014: 3086-3094 - 2013
- [i6]Thomas P. Minka, John D. Lafferty:
Expectation-Propogation for the Generative Aspect Model. CoRR abs/1301.0588 (2013) - [i5]Thomas P. Minka:
Expectation Propagation for approximate Bayesian inference. CoRR abs/1301.2294 (2013) - 2012
- [c34]Yoram Bachrach, Thore Graepel, Tom Minka, John Guiver:
How To Grade a Test Without Knowing the Answers - A Bayesian Graphical Model for Adaptive Crowdsourcing and Aptitude Testing. ICML 2012 - [c33]Souvik Sen, Bozidar Radunovic, Romit Roy Choudhury, Tom Minka:
You are facing the Mona Lisa: spot localization using PHY layer information. MobiSys 2012: 183-196 - [i4]Yuan (Alan) Qi, Ahmed H. Abdel-Gawad, Thomas P. Minka:
Sparse-posterior Gaussian Processes for general likelihoods. CoRR abs/1203.3507 (2012) - [i3]Thomas P. Minka, Rongjing Xiang, Yuan (Alan) Qi:
Virtual Vector Machine for Bayesian Online Classification. CoRR abs/1205.2623 (2012) - [i2]Max Welling, Thomas P. Minka, Yee Whye Teh:
Structured Region Graphs: Morphing EP into GBP. CoRR abs/1207.1426 (2012) - 2011
- [c32]Souvik Sen, Romit Roy Choudhury, Bozidar Radunovic, Tom Minka:
Precise indoor localization using PHY layer information. HotNets 2011: 18 - [c31]Souvik Sen, Bozidar Radunovic, Romit Roy Choudhury, Tom Minka:
Precise indoor localization using PHY information. MobiSys 2011: 413-414 - [c30]David A. Knowles, Tom Minka:
Non-conjugate Variational Message Passing for Multinomial and Binary Regression. NIPS 2011: 1701-1709 - 2010
- [c29]Yuan (Alan) Qi, Ahmed H. Abdel-Gawad, Thomas P. Minka:
Sparse-posterior Gaussian Processes for general likelihoods. UAI 2010: 450-457 - [c28]Zeyuan Allen Zhu, Weizhu Chen, Tom Minka, Chenguang Zhu, Zheng Chen:
A novel click model and its applications to online advertising. WSDM 2010: 321-330
2000 – 2009
- 2009
- [c27]Thomas P. Minka, Rongjing Xiang, Yuan (Alan) Qi:
Virtual Vector Machine for Bayesian Online Classification. UAI 2009: 411-418 - [c26]Fan Guo, Chao Liu, Anitha Kannan, Tom Minka, Michael J. Taylor, Yi Min Wang, Christos Faloutsos
:
Click chain model in web search. WWW 2009: 11-20 - [i1]Yuan (Alan) Qi, Ahmed H. Abdel-Gawad, Thomas P. Minka:
Variable sigma Gaussian processes: An expectation propagation perspective. CoRR abs/0910.0668 (2009) - 2008
- [c25]Peter V. Gehler, Carsten Rother, Andrew Blake, Thomas P. Minka, Toby Sharp:
Bayesian color constancy revisited. CVPR 2008 - [c24]Tom Minka, John M. Winn:
Gates. NIPS 2008: 1073-1080 - [c23]Michael J. Taylor, John Guiver, Stephen Robertson, Tom Minka:
SoftRank: optimizing non-smooth rank metrics. WSDM 2008: 77-86 - 2007
- [j6]Yuan (Alan) Qi, Thomas P. Minka:
Window-based expectation propagation for adaptive signal detection in flat-fading channels. IEEE Trans. Wirel. Commun. 6(1): 348-355 (2007) - [c22]Pierre Dangauthier, Ralf Herbrich, Tom Minka, Thore Graepel:
TrueSkill Through Time: Revisiting the History of Chess. NIPS 2007: 337-344 - 2006
- [c21]Julia A. Lasserre, Christopher M. Bishop, Thomas P. Minka:
Principled Hybrids of Generative and Discriminative Models. CVPR (1) 2006: 87-94 - [c20]Carsten Rother, Thomas P. Minka, Andrew Blake, Vladimir Kolmogorov:
Cosegmentation of Image Pairs by Histogram Matching - Incorporating a Global Constraint into MRFs. CVPR (1) 2006: 993-1000 - [c19]Ralf Herbrich, Tom Minka, Thore Graepel:
TrueSkillTM: A Bayesian Skill Rating System. NIPS 2006: 569-576 - 2005
- [c18]Yuan (Alan) Qi, Martin Szummer, Tom Minka:
Bayesian Conditional Random Fields. AISTATS 2005: 269-276 - [c17]Yuan (Alan) Qi, Martin Szummer, Thomas P. Minka:
Diagram Structure Recognition by Bayesian Conditional Random Fields. CVPR (2) 2005: 191-196 - [c16]John M. Winn, Antonio Criminisi, Thomas P. Minka:
Object Categorization by Learned Universal Visual Dictionary. ICCV 2005: 1800-1807 - [c15]Max Welling, Thomas P. Minka, Yee Whye Teh:
Structured Region Graphs: Morphing EP into GBP. UAI 2005: 609-614 - 2004
- [c14]Yuan (Alan) Qi, Thomas P. Minka, Rosalind W. Picard, Zoubin Ghahramani:
Predictive automatic relevance determination by expectation propagation. ICML 2004 - 2003
- [c13]Thomas P. Minka, Yuan (Alan) Qi:
Tree-structured Approximations by Expectation Propagation. NIPS 2003: 193-200 - [c12]Charles R. Rosenberg, Thomas P. Minka, Alok Ladsariya:
Bayesian Color Constancy with Non-Gaussian Models. NIPS 2003: 1595-1602 - 2002
- [c11]Yuan (Alan) Qi, Thomas P. Minka, Rosalind W. Picard:
Bayesian spectrum estimation of unevenly sampled nonstationary data. ICASSP 2002: 1473-1476 - [c10]Yi Zhang, James P. Callan, Thomas P. Minka:
Novelty and redundancy detection in adaptive filtering. SIGIR 2002: 81-88 - [c9]Thomas P. Minka, John D. Lafferty:
Expectation-Propogation for the Generative Aspect Model. UAI 2002: 352-359 - 2001
- [b1]Thomas P. Minka:
A family of algorithms for approximate Bayesian inference. Massachusetts Institute of Technology, Cambridge, MA, USA, 2001 - [j5]Thomas V. Papathomas, Ingemar J. Cox, Peter N. Yianilos, Matthew L. Miller, Thomas P. Minka, Tiffany E. Conway, Joumana Ghosn:
Psychophysical experiments on the PicHunter image retrieval system. J. Electronic Imaging 10(1): 170-180 (2001) - [c8]Thomas P. Minka, Dan S. Bloomberg, Kris Popat:
Document image decoding using iterated complete path search. Document Recognition and Retrieval 2001: 250-258 - [c7]Dan S. Bloomberg, Kris Popat, Thomas P. Minka:
Document Image Decoding Using Iterated Complete Path Search with Subsampled Heuristic Scoring. ICDAR 2001: 344-349 - [c6]Thomas P. Minka:
Expectation Propagation for approximate Bayesian inference. UAI 2001: 362-369 - 2000
- [j4]Ingemar J. Cox, Matthew L. Miller, Thomas P. Minka, Thomas V. Papathomas, Peter N. Yianilos:
The Bayesian image retrieval system, PicHunter: theory, implementation, and psychophysical experiments. IEEE Trans. Image Process. 9(1): 20-37 (2000) - [j3]Ingemar J. Cox, Matthew L. Miller, Thomas P. Minka, Thomas V. Papathomas, Peter N. Yianilos:
Correction to "the Bayesian image retrieval system, pichunter: theory, implementation, and psychophysical experiments". IEEE Trans. Image Process. 9(3): 524- (2000) - [c5]Thomas P. Minka:
Automatic Choice of Dimensionality for PCA. NIPS 2000: 598-604
1990 – 1999
- 1998
- [c4]Ingemar J. Cox, Matthew L. Miller, Thomas P. Minka, Peter N. Yianilos:
An Optimized Interaction Strategy for Bayesian Relevance Feedback. CVPR 1998: 553-558 - [c3]Thomas V. Papathomas, Tiffany E. Conway, Ingemar J. Cox, Joumana Ghosn, Matthew L. Miller, Thomas P. Minka, Peter N. Yianilos:
Psychophysical studies of the performance of an image database retrieval system. Human Vision and Electronic Imaging 1998: 591-602 - 1997
- [j2]Thomas P. Minka, Rosalind W. Picard:
Interactive learning with a "society of models". Pattern Recognit. 30(4): 565-581 (1997) - 1996
- [c2]Thomas P. Minka, Rosalind W. Picard:
Interactive Learning with a "Society of Models" . CVPR 1996: 447-452 - [c1]Rosalind W. Picard, Thomas P. Minka, Martin Szummer:
Modeling user subjectivity in image libraries. ICIP (2) 1996: 777-780 - 1995
- [j1]Rosalind W. Picard, Thomas P. Minka:
Vision Texture for Annotation. Multim. Syst. 3(1): 3-14 (1995)
Coauthor Index
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