Papers by Tom Minka (also available by topic)

Expectation propagation
 |   Bayesian methods
 |   |   Probabilistic modeling
 |   |   |   Optimization
 |   |   |   |   Text retrieval
 |   |   |   |   |   Computer vision
 |   |   |   |   |   |  
B 2014 Knowing what we don't know in NCAA Football ratings: Understanding and using structured uncertainty
B 2012 A Bayesian Graphical Model for Adaptive Crowdsourcing and Aptitude Testing
B 2012 Spot Localization using PHY Layer Information
B 2011 Non-conjugate Variational Message Passing for Multinomial and Binary Regression
E 2010 Sparse-posterior Gaussian Processes for general likelihoods
B T 2010 A Novel Click Model and Its Applications to Online Advertising
M 2009 Probabilistic Programming with Infer.NET
E 2009 Video lectures on Approximate Inference
B 2009 Automating variational inference for statistics and data mining
E 2009 Virtual Vector Machine for Bayesian Online Classification
B T 2009 Click Chain Model in Web Search
E 2008 Gates: A graphical notation for mixture models
T 2008 Selection bias in the LETOR datasets
B V 2008 Bayesian Color Constancy Revisited
T 2008 SoftRank: Optimising Non-Smooth Ranking Metrics
E B 2007 TrueSkill Through Time: Revisiting the History of Chess
T 2007 The Smoothed Dirichlet distribution: A new building block for generative topical models
E B 2006 TrueSkill: A Bayesian Skill Rating System
O 2006 Local Training and Belief Propagation
E 2006 Window-based expectation propagation for adaptive signal detection in flat-fading channels
B O V 2006 Cosegmentation of Image Pairs by Histogram Matching - Incorporating a Global Constraint into MRFs
M V 2006 Principled Hybrids of Generative and Discriminative Models
M 2005 Discriminative models, not discriminative training
B V 2005 Object Categorization by Learned Universal Visual Dictionary A Bayesian version of agglomerative information bottleneck.
E 2005 Structured Region Graphs: Morphing EP into GBP
E 2005 Divergence measures and message passing
E B 2005 Bayesian Conditional Random Fields
E 2004 Power EP
E 2004 A roadmap to research on EP
E B 2004 Predictive Automatic Relevance Determination by Expectation Propagation Preventing overfitting in ARD.
M V 2004 Exemplar-based likelihoods using the PDF projection theorem How to properly normalize distributions over image features.
M T V 2003 The `summation hack' as an outlier model An explanation of a common trick used in computer vision and text retrieval.
E 2003 Tree-structured approximations by expectation propagation
E 2003 Expectation Propagation for Infinite Mixtures
B V 2003 Bayesian Color Constancy with Non-Gaussian Models
E 2003 Expectation Propagation for Signal Detection in Flat-fading Channels
M 2003 Building statistical models by visualization
M 2003 Conjugate Analysis of the Conway-Maxwell-Poisson Distribution
M 2003 A Useful Distribution for Fitting Discrete Data: Revival of the COM-Poisson
M 2003 Computing with the COM-Poisson distribution
O 2002 Estimating a Gamma distribution
B 2002 Hessian-based Markov Chain Monte-Carlo Algorithms
E B 2002 Bayesian inference in dynamic models -- an overview
M 2002 Judging significance from error bars Something everyone should know how to do, but probably doesn't.
B 2002 Bayesian Spectrum Estimation of Unevenly Sampled Nonstationary Data
T 2002 Novelty and Redundancy Detection in Adaptive Filtering
E T 2002 Expectation-Propagation for the Generative Aspect Model
O 2003 A comparison of numerical optimizers for logistic regression Derives and compares eight methods, including iterative scaling.
E 2001 The EP energy function and minimization schemes
E 2001 Expectation Propagation for approximate Bayesian inference UAI version of my thesis, with some extra results.
E 2001 A family of algorithms for approximate Bayesian inference (PhD thesis work) A powerful generalization of belief propagation.
B 2001 Using lower bounds to approximate integrals A new interpretation and generalization of Variational Bayes.
O 2000 Beyond Newton's method Custom approximations for fast optimization.
O 2000 Estimating a Dirichlet distribution Optimization using Newton, modified Newton, and lower bounds.
B 2000 Automatic choice of dimensionality for PCA
B 2000 Bayesian model selection
B 2000 Deriving quadrature rules from Gaussian processes
B 2000 Distance measures as prior probabilities
B 2000 Bayesian model averaging is not model combination
B 2000 Empirical Risk Minimization is an incomplete inductive principle
M 1999 Learning How to Learn is Learning With Point Sets
B 1999 Linear regression with errors in both variables: A proper Bayesian approach Total least squares is not optimal.
M 1999 The Dirichlet-tree distribution The next time you use a Dirichlet, consider a Dirichlet-tree instead.
V 2001 Document image decoding using iterated complete path search
M 1998 From Hidden Markov Models to Linear Dynamical Systems
B 1998 Bayesian inference, entropy, and the multinomial distribution How empirical entropy and empirical mutual information can arise in Bayesian inference.
O 1998 Expectation-Maximization as lower bound maximization
B 1998 Bayesian linear regression
B 1998 Bayesian inference of a uniform distribution Bayesian methods succeed where maximum-likelihood does not.
B 1998 Inferring a Gaussian distribution Bayes provides a new approach to this age-old problem.
M 1998 Independence Diagrams A summary of Bayesian network notation.
B 1998 Pathologies of Orthodox Statistics
M 1998 Nuances of probability theory
B V 1998 An Optimized Interaction Strategy for Bayesian Relevance Feedback
M 1997 Old and New Matrix Algebra Useful for Statistics
V 1996 Modeling user subjectivity in image libraries
V 1996 An Image Database Browser that Learns from User Interaction
V 1997 Interactive Learning using a "Society of Models"
V 1995 Vision Texture for Annotation

Last modified: Tue Dec 14 16:10:47 GMT 2004