Scalable Model-Based Clustering with Sequential Monte Carlo
C. Trojan, Pavel Myshkov, P. Fearnhead, James Hensman, Tom Minka, Chris Nemeth
AISTATS 2026 | April 2026
Tom Minka
Senior Principal Researcher
论文与出版物
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Enterprise Alexandria: Online High-Precision Enterprise Knowledge Base Construction with Typed Entities
John Winn, Matteo Venanzi, Tom Minka, Ivan Korostelev, John Guiver, Elena Pochernina, Pavel Myshkov, Alex Spengler, Denise Wilkins, Siân Lindley, Richard Banks, Sam Webster, Yordan Zaykov
Automated Knowledge Base Construction | October 2021
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-
TrueSkill 2: An improved Bayesian skill rating system
Tom Minka, Ryan Cleven, Yordan Zaykov
MSR-TR-2018-8 | March 2018
作者：Microsoft
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-
-
Inference Algorithms for Probabilistic Graphical Models Video Lecture
December 2015
-
GroupBox: A Generative Model for Group Recommendation
Cheng Zhang, John Guiver, Tom Minka, Yordan Zaykov
MSR-TR-2015-61 | July 2015
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-
-
Elastic Distributed Bayesian Collaborative Filtering
Alex Beutel, Markus Weimer, Tom Minka, Yordan Zaykov, Vijay Narayanan
January 2014
-
Knowing what we don’t know in NCAA Football ratings: Understanding and using structured uncertainty
Daniel Tarlow, Thore Graepel, Tom Minka
MIT Sloan Sports Analytics Conference | January 2014
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-
-
Detecting Parameter Symmetries in Probabilistic Models
Robert Nishihara, Daniel Tarlow, Tom Minka
arXiv preprint arXiv:1312.5386 | December 2013
-
-
-
Spot Localization Using PHY Layer Information
Souvik Sen, Bozidar Radunovic, Romit Roy Choudhury, Tom Minka
In proceedings of ACM Mobisys | June 2012
-
How To Grade a Test Without Knowing the Answers – A Bayesian Graphical Model for Adaptive Crowdsourcing and Aptitude Testing
Yoram Bachrach, Tom Minka, John Guiver, Thore Graepel
29th International Conference on Machine Learning, Edinburgh, Scotland, UK | January 2012
-
-
-
Precise Indoor Localization using PHY Layer Information
Souvik Sen, Bozidar Radunovic, Romit Roy Choudhury, Tom Minka
ACM Hotnets | November 2011
-
Non-conjugate Variational Message Passing for Multinomial and Binary Regression
David A. Knowles, Tom Minka
Advances in Neural Information Processing Systems 24 | October 2011
-
-
-
Sparse-posterior Gaussian Processes for general likelihoods
Yuan Qi, Ahmed H. Abdel-Gawad, Tom Minka
Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence (UAI 2010) | July 2010
-
A Novel Click Model and Its Applications to Online Advertising
Zeyuan Allen Zhu, Weizhu Chen, Tom Minka, Chenguang Zhu, Zheng Chen
Proceedings of the Third ACM International Conference on Web Search and Data Mining (WSDM 2010) | February 2010
-
-
-
Approximate Inference Video Lecture Series
November 2009
Machine Learning Summer School 2009
-
Probabilistic Programming with Infer.NET
September 2009
-
Automating Variational Inference for Statistics and Data Mining
August 2009
Invited talk at the 74th Annual Meeting of the Psychometric Society (IMPS 2009)
-
Click Chain Model in Web Search
Fan Guo, Chao Liu, Anitha Kannan, Tom Minka, Mike Taylor, Yi-Min Wang, Christos Faloutsos
WWW'09: Proceedings of the 18th International World Wide Web Conference | April 2009
-
Virtual Vector Machine for Bayesian Online Classification
Tom Minka, Rongjing Xiang, Yuan (Alan) Qi
UAI-P-2009-PG-411-418 | April 2009
Symposium on Advances in Digital Filtering and Signal Processing
-
Gates
Advances in Neural Information Processing Systems 21 | January 2009
Advances in Neural Information Processing Systems 21
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-
-
Gates: A Graphical Notation for Mixture Models
MSR-TR-2008-185 | December 2008
-
Selection Bias in the LETOR Datasets
August 2008
-
SoftRank: Optimising Non-Smooth Rank Metrics
Mike Taylor, John Guiver, Stephen Robertson, Tom Minka
WSDM 2008 | February 2008
-
Bayesian Color Constancy Revisited
Peter Vincent Gehler, Carsten Rother, Andrew Blake, Tom Minka, Toby Sharp
CVPR | January 2008
-
TrueSkill Through Time: Revisiting the History of Chess
Ralf Herbrich, Tom Minka, Thore Graepel
Advances in Neural Information Processing Systems 20 | January 2008
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-
-
The EP Energy Function and Minimization Schemes
August 2007
-
The Smoothed-Dirichlet Distribution: A New Building Block for Generative Topic Models
Ramesh Nallapati, Tom Minka, Stephen Robertson
April 2007
-
Bayesian Inference in Dynamic Models
March 2007
-
TrueSkill(TM): A Bayesian Skill Rating System
Ralf Herbrich, Tom Minka, Thore Graepel
Advances in Neural Information Processing Systems 20 | January 2007
-
Window-Based Expectation Propagation for Adaptive Signal Detection in Flat-Fading Channels
Yuan Qi, Tom Minka
IEEE Transactions on Wireless Communications | January 2007
-
-
-
Local Training and Belief Propagation
Charles Sutton, Tom Minka
MSR-TR-2006-121 | August 2006
-
Principled Hybrids of Generative and Discriminative Models
Julia A. Lasserre, Christopher Bishop, Tom Minka
IEEE Conference on Computer Vision and Pattern Recognition | June 2006
-
Cosegmenting Image Pairs by Matching Global Histograms
Carsten Rother, Vladimir Kolmogorov, Tom Minka, Andrew Blake
Proc. IEEE Computer Vision and Pattern Recognition (CVPR) | June 2006
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-
-
Discriminative models, not discriminative training
MSR-TR-2005-144 | October 2005
-
Object Categorization by Learned Universal Visual Dictionary
John Winn, Antonio Criminisi, Tom Minka
August 2005
-
Diagram Structure Recognition by Bayesian Conditional Random Fields
Yuan Qi, Martin Szummer, Tom Minka
Proceedings of Computer Vision Pattern Recognition (CVPR) | June 2005
-
A Useful Distribution for Fitting Discrete Data: Revival of the Conway-Maxwell-Poisson Distribution
Galit Shmueli, Tom Minka, Joseph B. Kadane, Sharad Borle, Peter Boatwright
Journal of the Royal Statistical Society: Series C (Applied Statistics) | January 2005, 第 54 卷(1): pp. 127-142
-
Bayesian Conditional Random Fields
Yuan Qi, Martin Szummer, Tom Minka
Journal of Machine Learning Research (JMLR), AI & Statistics | January 2005, pp. 269-276
-
Divergence Measures and Message Passing
MSR-TR-2005-173 | January 2005
-
Object Categorization by Learned Universal Visual Dictionary
John Winn, Antonio Criminisi, Tom Minka
Proceedings IEEE Intl. Conf. on Computer Vision (ICCV). | January 2005
-
Structured Region Graphs: Morphing EP into GBP
Max Welling, Tom Minka, Yee Whye Teh
UAI | January 2005
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-
-
A Roadmap to Research on EP
August 2004
-
Exemplar-based Likelihoods Using the PDF Projection Theorem
MSR-TR-2004-148 | January 2004
-
Expectation Propagation for Infinite Mixtures
Tom Minka, Zoubin Ghahramani
NIPS 2003 | January 2004
-
Power EP
MSR-TR-2004-149 | January 2004
-
Predictive Automatic Relevance Determination by Expectation Propagation
Yuan (Alan) Qi, Tom Minka, Rosalind W. Picard, Zoubin Ghahramani
ICML | January 2004
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-
The ‘Summation Hack’ as an Outlier Model
August 2003
-
Computing with the COM-Poisson Distribution
Tom Minka, Galit Shmueli, Joseph B. Kadane, Sharad Borle, Peter Boatwright
June 2003
-
Building Statistical Models by Visualization
May 2003
-
Tree-structured Approximations by Expectation Propagation
Tom Minka, Yuan Qi
NIPS 2003 | April 2003
-
A Comparison of Numerical Optimizers for Logistic Regression
March 2003
-
Bayesian Inference, Entropy, and the Multinomial Distribution
January 2003
-
Bayesian Color Constancy with Non-Gaussian Models
Charles Rosenberg, Tom Minka, Alok Ladsariya
NIPS2003 | January 2003
-
-
-
Judging Significance from Error Bars
November 2002
-
Expectation Propagation for Signal Detection in Flat-fading Channels
Yuan Qi, Tom Minka
November 2002
-
Bayesian Spectrum Estimation of Unevenly Sampled Nonstationary Data
Yuan Qi, Tom Minka, Rosalind W. Picard
October 2002
-
Hessian-based Markov Chain Monte-Carlo Algorithms
Yuan Qi, Tom Minka
September 2002
-
Estimating a Gamma Distribution
April 2002
-
Expectation-Propagation for the Generative Aspect Model
Tom Minka, John Lafferty
Proceedings of the 18th Conference on Uncertainty in Artificial Intelligence | March 2002
-
-
-
Using Lower Bounds to Approximate Integrals
August 2001
-
Expectation Propagation for Approximate Bayesian Inference
April 2001
April 2001
-
Bayesian Inference of a Uniform Distribution
January 2001
-
Pathologies of Orthodox Statistics
November 2001
-
-
-
Bayesian Model Selection
September 2000
-
Estimating a Dirichlet Distribution
September 2000
-
Distance Measures as Prior Probabilities
September 2000
-
Empirical Risk Minimization is an Incomplete Inductive Principle
August 2000
-
Bayesian Linear Regression
July 2000
-
Bayesian Model Averaging is not Model Combination
July 2000
-
Deriving Quadrature Rules from Gaussian Processes
June 2000
-
Beyond Newton’s Method
April 2000
-
Automatic Choice of Dimensionality for PCA
MIT-MED-514 | January 2000
-
-
-
Expectation-Maximization as Lower Bound Maximization
November 1999
-
Linear Regression with Errors in Both Variables: A Proper Bayesian Approach
October 1999
-
From Hidden Markov Models to Linear Dynamical Systems
July 1999
-
Learning How to Learn is Learning With Point Sets
Tom Minka, Rosalind W. Picard
July 1999
-
The Dirichlet-tree Distribution
July 1999
-
-
-
Independence Diagrams
August 1998
-
Inferring a Gaussian Distribution
August 1998
-
Nuances of Probability Theory
July 1998
-
-
-
Old and New Matrix Algebra Useful for Statistics
September 1997
-
Interactive Learning Using a “Society of Models”
Tom Minka, Rosalind W. Picard
TR-349-ABSTRACT | May 1997
Best Paper Award
-
-
-
Modeling User Subjectivity In Image Libraries
Rosalind W. Picard, Tom Minka, Martin Szummer
IEEE Intl. Conf. on Image Processing (ICIP) | September 1996
-
An Image Database Browser that Learns from User Interaction
January 1996
January 1996
-
-
-
Modeling User Subjectivity in Image Libraries
IEEE International Conference on Image Processing (ICIP 96), Lausanne, Switzerland | September 1995
-
Vision Texture for Annotation
Rosalind W. Picard, Tom Minka
April 1995
-
-
-
Scalable Model-Based Clustering with Sequential Monte Carlo
C. Trojan, Pavel Myshkov, P. Fearnhead, James Hensman, Tom Minka, Chris Nemeth
AISTATS 2026 | April 2026
-
Enterprise Alexandria: Online High-Precision Enterprise Knowledge Base Construction with Typed Entities
John Winn, Matteo Venanzi, Tom Minka, Ivan Korostelev, John Guiver, Elena Pochernina, Pavel Myshkov, Alex Spengler, Denise Wilkins, Siân Lindley, Richard Banks, Sam Webster, Yordan Zaykov
Automated Knowledge Base Construction | October 2021
-
TrueSkill 2: An improved Bayesian skill rating system
Tom Minka, Ryan Cleven, Yordan Zaykov
MSR-TR-2018-8 | March 2018
作者：Microsoft
-
Belief Propagation with Strings
Boris Yangel, Tom Minka, John Winn
MSR-TR-2017-11 | February 2017
-
GroupBox: A Generative Model for Group Recommendation
Cheng Zhang, John Guiver, Tom Minka, Yordan Zaykov
MSR-TR-2015-61 | July 2015
-
Elastic Distributed Bayesian Collaborative Filtering
Alex Beutel, Markus Weimer, Tom Minka, Yordan Zaykov, Vijay Narayanan
January 2014
-
Knowing what we don’t know in NCAA Football ratings: Understanding and using structured uncertainty
Daniel Tarlow, Thore Graepel, Tom Minka
MIT Sloan Sports Analytics Conference | January 2014
-
How To Grade a Test Without Knowing the Answers – A Bayesian Graphical Model for Adaptive Crowdsourcing and Aptitude Testing
Yoram Bachrach, Tom Minka, John Guiver, Thore Graepel
29th International Conference on Machine Learning, Edinburgh, Scotland, UK | January 2012
-
Sparse-posterior Gaussian Processes for general likelihoods
Yuan Qi, Ahmed H. Abdel-Gawad, Tom Minka
Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence (UAI 2010) | July 2010
-
A Novel Click Model and Its Applications to Online Advertising
Zeyuan Allen Zhu, Weizhu Chen, Tom Minka, Chenguang Zhu, Zheng Chen
Proceedings of the Third ACM International Conference on Web Search and Data Mining (WSDM 2010) | February 2010
-
Virtual Vector Machine for Bayesian Online Classification
Tom Minka, Rongjing Xiang, Yuan (Alan) Qi
UAI-P-2009-PG-411-418 | April 2009
Symposium on Advances in Digital Filtering and Signal Processing
-
Click Chain Model in Web Search
Fan Guo, Chao Liu, Anitha Kannan, Tom Minka, Mike Taylor, Yi-Min Wang, Christos Faloutsos
WWW'09: Proceedings of the 18th International World Wide Web Conference | April 2009
-
Gates
Advances in Neural Information Processing Systems 21 | January 2009
Advances in Neural Information Processing Systems 21
-
Gates: A Graphical Notation for Mixture Models
MSR-TR-2008-185 | December 2008
-
Bayesian Color Constancy Revisited
Peter Vincent Gehler, Carsten Rother, Andrew Blake, Tom Minka, Toby Sharp
CVPR | January 2008
-
TrueSkill Through Time: Revisiting the History of Chess
Ralf Herbrich, Tom Minka, Thore Graepel
Advances in Neural Information Processing Systems 20 | January 2008
-
Window-Based Expectation Propagation for Adaptive Signal Detection in Flat-Fading Channels
Yuan Qi, Tom Minka
IEEE Transactions on Wireless Communications | January 2007
-
TrueSkill(TM): A Bayesian Skill Rating System
Ralf Herbrich, Tom Minka, Thore Graepel
Advances in Neural Information Processing Systems 20 | January 2007
-
Principled Hybrids of Generative and Discriminative Models
Julia A. Lasserre, Christopher Bishop, Tom Minka
IEEE Conference on Computer Vision and Pattern Recognition | June 2006
-
Cosegmenting Image Pairs by Matching Global Histograms
Carsten Rother, Vladimir Kolmogorov, Tom Minka, Andrew Blake
Proc. IEEE Computer Vision and Pattern Recognition (CVPR) | June 2006
-
Discriminative models, not discriminative training
MSR-TR-2005-144 | October 2005
-
Object Categorization by Learned Universal Visual Dictionary
John Winn, Antonio Criminisi, Tom Minka
Proceedings IEEE Intl. Conf. on Computer Vision (ICCV). | January 2005
-
Bayesian Conditional Random Fields
Yuan Qi, Martin Szummer, Tom Minka
Journal of Machine Learning Research (JMLR), AI & Statistics | January 2005, pp. 269-276
-
A Roadmap to Research on EP
August 2004
-
Predictive Automatic Relevance Determination by Expectation Propagation
Yuan (Alan) Qi, Tom Minka, Rosalind W. Picard, Zoubin Ghahramani
ICML | January 2004
-
-
-
Enterprise Alexandria: Online High-Precision Enterprise Knowledge Base Construction with Typed Entities
John Winn, Matteo Venanzi, Tom Minka, Ivan Korostelev, John Guiver, Elena Pochernina, Pavel Myshkov, Alex Spengler, Denise Wilkins, Siân Lindley, Richard Banks, Sam Webster, Yordan Zaykov
Automated Knowledge Base Construction | October 2021
-
-
-
Using Lower Bounds to Approximate Integrals
August 2001
-
Expectation Propagation for Approximate Bayesian Inference
April 2001
April 2001
-
Bayesian Inference of a Uniform Distribution
January 2001
-
Bayesian Model Selection
September 2000
-
Estimating a Dirichlet Distribution
September 2000
-
Distance Measures as Prior Probabilities
September 2000
-
Empirical Risk Minimization is an Incomplete Inductive Principle
August 2000
-
Bayesian Linear Regression
July 2000
-
Bayesian Model Averaging is not Model Combination
July 2000
-
Deriving Quadrature Rules from Gaussian Processes
June 2000
-
Beyond Newton’s Method
April 2000
-
Automatic Choice of Dimensionality for PCA
MIT-MED-514 | January 2000
-
Expectation-Maximization as Lower Bound Maximization
November 1999
-
Linear Regression with Errors in Both Variables: A Proper Bayesian Approach
October 1999
-
From Hidden Markov Models to Linear Dynamical Systems
July 1999
-
The Dirichlet-tree Distribution
July 1999
-
Learning How to Learn is Learning With Point Sets
Tom Minka, Rosalind W. Picard
July 1999
-
Independence Diagrams
August 1998
-
Inferring a Gaussian Distribution
August 1998
-
Nuances of Probability Theory
July 1998
-
Old and New Matrix Algebra Useful for Statistics
September 1997
-
An Image Database Browser that Learns from User Interaction
January 1996
January 1996
-
Inference Algorithms for Probabilistic Graphical Models Video Lecture
December 2015
-
Detecting Parameter Symmetries in Probabilistic Models
Robert Nishihara, Daniel Tarlow, Tom Minka
arXiv preprint arXiv:1312.5386 | December 2013
-
Non-conjugate Variational Message Passing for Multinomial and Binary Regression
David A. Knowles, Tom Minka
Advances in Neural Information Processing Systems 24 | October 2011
-
Approximate Inference Video Lecture Series
November 2009
Machine Learning Summer School 2009
-
Probabilistic Programming with Infer.NET
September 2009
-
Automating Variational Inference for Statistics and Data Mining
August 2009
Invited talk at the 74th Annual Meeting of the Psychometric Society (IMPS 2009)
-
SoftRank: Optimising Non-Smooth Rank Metrics
Mike Taylor, John Guiver, Stephen Robertson, Tom Minka
WSDM 2008 | February 2008
-
The EP Energy Function and Minimization Schemes
August 2007
-
Bayesian Inference in Dynamic Models
March 2007
-
Local Training and Belief Propagation
Charles Sutton, Tom Minka
MSR-TR-2006-121 | August 2006
-
A Useful Distribution for Fitting Discrete Data: Revival of the Conway-Maxwell-Poisson Distribution
Galit Shmueli, Tom Minka, Joseph B. Kadane, Sharad Borle, Peter Boatwright
Journal of the Royal Statistical Society: Series C (Applied Statistics) | January 2005, 第 54 卷(1): pp. 127-142
-
Structured Region Graphs: Morphing EP into GBP
Max Welling, Tom Minka, Yee Whye Teh
UAI | January 2005
-
Expectation Propagation for Infinite Mixtures
Tom Minka, Zoubin Ghahramani
NIPS 2003 | January 2004
-
Exemplar-based Likelihoods Using the PDF Projection Theorem
MSR-TR-2004-148 | January 2004
-
The ‘Summation Hack’ as an Outlier Model
August 2003
-
Computing with the COM-Poisson Distribution
Tom Minka, Galit Shmueli, Joseph B. Kadane, Sharad Borle, Peter Boatwright
June 2003
-
Building Statistical Models by Visualization
May 2003
-
Tree-structured Approximations by Expectation Propagation
Tom Minka, Yuan Qi
NIPS 2003 | April 2003
-
A Comparison of Numerical Optimizers for Logistic Regression
March 2003
-
Bayesian Inference, Entropy, and the Multinomial Distribution
January 2003
-
Judging Significance from Error Bars
November 2002
-
Expectation Propagation for Signal Detection in Flat-fading Channels
Yuan Qi, Tom Minka
November 2002
-
Bayesian Spectrum Estimation of Unevenly Sampled Nonstationary Data
Yuan Qi, Tom Minka, Rosalind W. Picard
October 2002
-
Hessian-based Markov Chain Monte-Carlo Algorithms
Yuan Qi, Tom Minka
September 2002
-
Estimating a Gamma Distribution
April 2002
-
Expectation-Propagation for the Generative Aspect Model
Tom Minka, John Lafferty
Proceedings of the 18th Conference on Uncertainty in Artificial Intelligence | March 2002
-
Pathologies of Orthodox Statistics
November 2001
-
-
-
A Novel Click Model and Its Applications to Online Advertising
Zeyuan Allen Zhu, Weizhu Chen, Tom Minka, Chenguang Zhu, Zheng Chen
Proceedings of the Third ACM International Conference on Web Search and Data Mining (WSDM 2010) | February 2010
-
Click Chain Model in Web Search
Fan Guo, Chao Liu, Anitha Kannan, Tom Minka, Mike Taylor, Yi-Min Wang, Christos Faloutsos
WWW'09: Proceedings of the 18th International World Wide Web Conference | April 2009
-
Selection Bias in the LETOR Datasets
August 2008
-
The Smoothed-Dirichlet Distribution: A New Building Block for Generative Topic Models
Ramesh Nallapati, Tom Minka, Stephen Robertson
April 2007
-
-
-
Interactive Learning Using a “Society of Models”
Tom Minka, Rosalind W. Picard
TR-349-ABSTRACT | May 1997
Best Paper Award
-
Modeling User Subjectivity in Image Libraries
IEEE International Conference on Image Processing (ICIP 96), Lausanne, Switzerland | September 1995
-
Vision Texture for Annotation
Rosalind W. Picard, Tom Minka
April 1995
-
Detecting Parameter Symmetries in Probabilistic Models
Robert Nishihara, Daniel Tarlow, Tom Minka
arXiv preprint arXiv:1312.5386 | December 2013
-
Bayesian Color Constancy Revisited
Peter Vincent Gehler, Carsten Rother, Andrew Blake, Tom Minka, Toby Sharp
CVPR | January 2008
-
Object Categorization by Learned Universal Visual Dictionary
John Winn, Antonio Criminisi, Tom Minka
August 2005
-
Bayesian Color Constancy with Non-Gaussian Models
Charles Rosenberg, Tom Minka, Alok Ladsariya
NIPS2003 | January 2003
-
-
-
Spot Localization Using PHY Layer Information
Souvik Sen, Bozidar Radunovic, Romit Roy Choudhury, Tom Minka
In proceedings of ACM Mobisys | June 2012
-
Precise Indoor Localization using PHY Layer Information
Souvik Sen, Bozidar Radunovic, Romit Roy Choudhury, Tom Minka
ACM Hotnets | November 2011
-
Diagram Structure Recognition by Bayesian Conditional Random Fields
Yuan Qi, Martin Szummer, Tom Minka
Proceedings of Computer Vision Pattern Recognition (CVPR) | June 2005
-
-
-
Non-conjugate Variational Message Passing for Multinomial and Binary Regression
David A. Knowles, Tom Minka
Advances in Neural Information Processing Systems 24 | October 2011
-
-
-
Modeling User Subjectivity In Image Libraries
Rosalind W. Picard, Tom Minka, Martin Szummer
IEEE Intl. Conf. on Image Processing (ICIP) | September 1996
-
Local Training and Belief Propagation
Charles Sutton, Tom Minka
MSR-TR-2006-121 | August 2006
-
Divergence Measures and Message Passing
MSR-TR-2005-173 | January 2005
-
-
-
-
Using Lower Bounds to Approximate Integrals
August 2001
-
Bayesian Inference of a Uniform Distribution
January 2001
-
Bayesian Model Selection
September 2000
-
Estimating a Dirichlet Distribution
September 2000
-
Distance Measures as Prior Probabilities
September 2000
-
Empirical Risk Minimization is an Incomplete Inductive Principle
August 2000
-
Bayesian Linear Regression
July 2000
-
Bayesian Model Averaging is not Model Combination
July 2000
-
Deriving Quadrature Rules from Gaussian Processes
June 2000
-
Beyond Newton’s Method
April 2000
-
Expectation-Maximization as Lower Bound Maximization
November 1999
-
Linear Regression with Errors in Both Variables: A Proper Bayesian Approach
October 1999
-
From Hidden Markov Models to Linear Dynamical Systems
July 1999
-
The Dirichlet-tree Distribution
July 1999
-
Learning How to Learn is Learning With Point Sets
Tom Minka, Rosalind W. Picard
July 1999
-
Independence Diagrams
August 1998
-
Inferring a Gaussian Distribution
August 1998
-
Nuances of Probability Theory
July 1998
-
Old and New Matrix Algebra Useful for Statistics
September 1997
-
Modeling User Subjectivity In Image Libraries
Rosalind W. Picard, Tom Minka, Martin Szummer
IEEE Intl. Conf. on Image Processing (ICIP) | September 1996
-
Modeling User Subjectivity in Image Libraries
IEEE International Conference on Image Processing (ICIP 96), Lausanne, Switzerland | September 1995
-
Vision Texture for Annotation
Rosalind W. Picard, Tom Minka
April 1995
-
Scalable Model-Based Clustering with Sequential Monte Carlo
C. Trojan, Pavel Myshkov, P. Fearnhead, James Hensman, Tom Minka, Chris Nemeth
AISTATS 2026 | April 2026
-
Enterprise Alexandria: Online High-Precision Enterprise Knowledge Base Construction with Typed Entities
John Winn, Matteo Venanzi, Tom Minka, Ivan Korostelev, John Guiver, Elena Pochernina, Pavel Myshkov, Alex Spengler, Denise Wilkins, Siân Lindley, Richard Banks, Sam Webster, Yordan Zaykov
Automated Knowledge Base Construction | October 2021
-
Elastic Distributed Bayesian Collaborative Filtering
Alex Beutel, Markus Weimer, Tom Minka, Yordan Zaykov, Vijay Narayanan
January 2014
-
Knowing what we don’t know in NCAA Football ratings: Understanding and using structured uncertainty
Daniel Tarlow, Thore Graepel, Tom Minka
MIT Sloan Sports Analytics Conference | January 2014
-
Detecting Parameter Symmetries in Probabilistic Models
Robert Nishihara, Daniel Tarlow, Tom Minka
arXiv preprint arXiv:1312.5386 | December 2013
-
Spot Localization Using PHY Layer Information
Souvik Sen, Bozidar Radunovic, Romit Roy Choudhury, Tom Minka
In proceedings of ACM Mobisys | June 2012
-
How To Grade a Test Without Knowing the Answers – A Bayesian Graphical Model for Adaptive Crowdsourcing and Aptitude Testing
Yoram Bachrach, Tom Minka, John Guiver, Thore Graepel
29th International Conference on Machine Learning, Edinburgh, Scotland, UK | January 2012
-
Precise Indoor Localization using PHY Layer Information
Souvik Sen, Bozidar Radunovic, Romit Roy Choudhury, Tom Minka
ACM Hotnets | November 2011
-
Non-conjugate Variational Message Passing for Multinomial and Binary Regression
David A. Knowles, Tom Minka
Advances in Neural Information Processing Systems 24 | October 2011
-
Sparse-posterior Gaussian Processes for general likelihoods
Yuan Qi, Ahmed H. Abdel-Gawad, Tom Minka
Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence (UAI 2010) | July 2010
-
A Novel Click Model and Its Applications to Online Advertising
Zeyuan Allen Zhu, Weizhu Chen, Tom Minka, Chenguang Zhu, Zheng Chen
Proceedings of the Third ACM International Conference on Web Search and Data Mining (WSDM 2010) | February 2010
-
Probabilistic Programming with Infer.NET
September 2009
-
Click Chain Model in Web Search
Fan Guo, Chao Liu, Anitha Kannan, Tom Minka, Mike Taylor, Yi-Min Wang, Christos Faloutsos
WWW'09: Proceedings of the 18th International World Wide Web Conference | April 2009
-
Gates
Advances in Neural Information Processing Systems 21 | January 2009
Advances in Neural Information Processing Systems 21
-
Selection Bias in the LETOR Datasets
August 2008
-
SoftRank: Optimising Non-Smooth Rank Metrics
Mike Taylor, John Guiver, Stephen Robertson, Tom Minka
WSDM 2008 | February 2008
-
Bayesian Color Constancy Revisited
Peter Vincent Gehler, Carsten Rother, Andrew Blake, Tom Minka, Toby Sharp
CVPR | January 2008
-
TrueSkill Through Time: Revisiting the History of Chess
Ralf Herbrich, Tom Minka, Thore Graepel
Advances in Neural Information Processing Systems 20 | January 2008
-
The EP Energy Function and Minimization Schemes
August 2007
-
The Smoothed-Dirichlet Distribution: A New Building Block for Generative Topic Models
Ramesh Nallapati, Tom Minka, Stephen Robertson
April 2007
-
Bayesian Inference in Dynamic Models
March 2007
-
TrueSkill(TM): A Bayesian Skill Rating System
Ralf Herbrich, Tom Minka, Thore Graepel
Advances in Neural Information Processing Systems 20 | January 2007
-
Principled Hybrids of Generative and Discriminative Models
Julia A. Lasserre, Christopher Bishop, Tom Minka
IEEE Conference on Computer Vision and Pattern Recognition | June 2006
-
Cosegmenting Image Pairs by Matching Global Histograms
Carsten Rother, Vladimir Kolmogorov, Tom Minka, Andrew Blake
Proc. IEEE Computer Vision and Pattern Recognition (CVPR) | June 2006
-
Object Categorization by Learned Universal Visual Dictionary
John Winn, Antonio Criminisi, Tom Minka
August 2005
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Diagram Structure Recognition by Bayesian Conditional Random Fields
Yuan Qi, Martin Szummer, Tom Minka
Proceedings of Computer Vision Pattern Recognition (CVPR) | June 2005
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Structured Region Graphs: Morphing EP into GBP
Max Welling, Tom Minka, Yee Whye Teh
UAI | January 2005
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Object Categorization by Learned Universal Visual Dictionary
John Winn, Antonio Criminisi, Tom Minka
Proceedings IEEE Intl. Conf. on Computer Vision (ICCV). | January 2005
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A Roadmap to Research on EP
August 2004
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Expectation Propagation for Infinite Mixtures
Tom Minka, Zoubin Ghahramani
NIPS 2003 | January 2004
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Predictive Automatic Relevance Determination by Expectation Propagation
Yuan (Alan) Qi, Tom Minka, Rosalind W. Picard, Zoubin Ghahramani
ICML | January 2004
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The ‘Summation Hack’ as an Outlier Model
August 2003
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Computing with the COM-Poisson Distribution
Tom Minka, Galit Shmueli, Joseph B. Kadane, Sharad Borle, Peter Boatwright
June 2003
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Building Statistical Models by Visualization
May 2003
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Tree-structured Approximations by Expectation Propagation
Tom Minka, Yuan Qi
NIPS 2003 | April 2003
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A Comparison of Numerical Optimizers for Logistic Regression
March 2003
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Bayesian Inference, Entropy, and the Multinomial Distribution
January 2003
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Bayesian Color Constancy with Non-Gaussian Models
Charles Rosenberg, Tom Minka, Alok Ladsariya
NIPS2003 | January 2003
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Judging Significance from Error Bars
November 2002
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Expectation Propagation for Signal Detection in Flat-fading Channels
Yuan Qi, Tom Minka
November 2002
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Bayesian Spectrum Estimation of Unevenly Sampled Nonstationary Data
Yuan Qi, Tom Minka, Rosalind W. Picard
October 2002
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Hessian-based Markov Chain Monte-Carlo Algorithms
Yuan Qi, Tom Minka
September 2002
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Estimating a Gamma Distribution
April 2002
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Expectation-Propagation for the Generative Aspect Model
Tom Minka, John Lafferty
Proceedings of the 18th Conference on Uncertainty in Artificial Intelligence | March 2002
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Pathologies of Orthodox Statistics
November 2001
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Automatic Choice of Dimensionality for PCA
MIT-MED-514 | January 2000
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Interactive Learning Using a “Society of Models”
Tom Minka, Rosalind W. Picard
TR-349-ABSTRACT | May 1997
Best Paper Award
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TrueSkill 2: An improved Bayesian skill rating system
Tom Minka, Ryan Cleven, Yordan Zaykov
MSR-TR-2018-8 | March 2018
作者：Microsoft
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Belief Propagation with Strings
Boris Yangel, Tom Minka, John Winn
MSR-TR-2017-11 | February 2017
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GroupBox: A Generative Model for Group Recommendation
Cheng Zhang, John Guiver, Tom Minka, Yordan Zaykov
MSR-TR-2015-61 | July 2015
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Virtual Vector Machine for Bayesian Online Classification
Tom Minka, Rongjing Xiang, Yuan (Alan) Qi
UAI-P-2009-PG-411-418 | April 2009
Symposium on Advances in Digital Filtering and Signal Processing
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Gates: A Graphical Notation for Mixture Models
MSR-TR-2008-185 | December 2008
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Local Training and Belief Propagation
Charles Sutton, Tom Minka
MSR-TR-2006-121 | August 2006
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Discriminative models, not discriminative training
MSR-TR-2005-144 | October 2005
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Divergence Measures and Message Passing
MSR-TR-2005-173 | January 2005
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Power EP
MSR-TR-2004-149 | January 2004
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Exemplar-based Likelihoods Using the PDF Projection Theorem
MSR-TR-2004-148 | January 2004
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Window-Based Expectation Propagation for Adaptive Signal Detection in Flat-Fading Channels
Yuan Qi, Tom Minka
IEEE Transactions on Wireless Communications | January 2007
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A Useful Distribution for Fitting Discrete Data: Revival of the Conway-Maxwell-Poisson Distribution
Galit Shmueli, Tom Minka, Joseph B. Kadane, Sharad Borle, Peter Boatwright
Journal of the Royal Statistical Society: Series C (Applied Statistics) | January 2005, 第 54 卷(1): pp. 127-142
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Bayesian Conditional Random Fields
Yuan Qi, Martin Szummer, Tom Minka
Journal of Machine Learning Research (JMLR), AI & Statistics | January 2005, pp. 269-276
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An Image Database Browser that Learns from User Interaction
January 1996
January 1996
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Inference Algorithms for Probabilistic Graphical Models Video Lecture
December 2015
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Approximate Inference Video Lecture Series
November 2009
Machine Learning Summer School 2009
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Automating Variational Inference for Statistics and Data Mining
August 2009
Invited talk at the 74th Annual Meeting of the Psychometric Society (IMPS 2009)
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Expectation Propagation for Approximate Bayesian Inference
April 2001
April 2001
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