Papers by Tom Minka (also available
by date
)
Expectation Propagation
Sparse-posterior Gaussian Processes for general likelihoods
(2010)
Video lectures on Approximate Inference
(2009)
Virtual Vector Machine for Bayesian Online Classification
(2009)
Gates: A graphical notation for mixture models
(2008)
TrueSkill Through Time: Revisiting the History of Chess
(2007)
TrueSkill: A Bayesian Skill Rating System
(2006)
Window-based expectation propagation for adaptive signal detection in flat-fading channels
(2006)
Structured Region Graphs: Morphing EP into GBP
(2005)
Divergence measures and message passing
(2005)
Bayesian Conditional Random Fields
(2005)
Power EP
(2004)
A roadmap to research on EP
(2004)
Predictive Automatic Relevance Determination by Expectation Propagation
(2004)
Preventing overfitting in ARD.
Tree-structured approximations by expectation propagation
(2003)
Expectation Propagation for Infinite Mixtures
(2003)
Expectation Propagation for Signal Detection in Flat-fading Channels
(2003)
Bayesian inference in dynamic models -- an overview
(2002)
Expectation-Propagation for the Generative Aspect Model
(2002)
The EP energy function and minimization schemes
(2001)
Expectation Propagation for approximate Bayesian inference
(2001)
UAI version of my thesis, with some extra results.
A family of algorithms for approximate Bayesian inference
(2001)
(PhD thesis work) A powerful generalization of belief propagation.
Bayesian methods
A Bayesian Graphical Model for Adaptive Crowdsourcing and Aptitude Testing
(2012)
Spot Localization using PHY Layer Information
(2012)
Non-conjugate Variational Message Passing for Multinomial and Binary Regression
(2011)
Automating variational inference for statistics and data mining
(2009)
Hessian-based Markov Chain Monte-Carlo Algorithms
(2002)
Bayesian Spectrum Estimation of Unevenly Sampled Nonstationary Data
(2002)
Using lower bounds to approximate integrals
(2001)
A new interpretation and generalization of Variational Bayes.
Automatic choice of dimensionality for PCA
(2000)
Bayesian model selection
(2000)
Deriving quadrature rules from Gaussian processes
(2000)
Distance measures as prior probabilities
(2000)
Bayesian model averaging is not model combination
(2000)
Empirical Risk Minimization is an incomplete inductive principle
(2000)
Linear regression with errors in both variables: A proper Bayesian approach
(1999)
Total least squares is not optimal.
Bayesian inference, entropy, and the multinomial distribution
(1998)
How empirical entropy and empirical mutual information can arise in Bayesian inference.
Bayesian linear regression
(1998)
Bayesian inference of a uniform distribution
(1998)
Bayesian methods succeed where maximum-likelihood does not.
Inferring a Gaussian distribution
(1998)
Bayes provides a new approach to this age-old problem.
Pathologies of Orthodox Statistics
(1998)
Computer vision
Bayesian Color Constancy Revisited
(2008)
Cosegmentation of Image Pairs by Histogram Matching - Incorporating a Global Constraint into MRFs
(2006)
Principled Hybrids of Generative and Discriminative Models
(2006)
Object Categorization by Learned Universal Visual Dictionary
(2005)
A Bayesian version of agglomerative information bottleneck.
Exemplar-based likelihoods using the PDF projection theorem
(2004)
How to properly normalize distributions over image features.
The `summation hack' as an outlier model
(2003)
An explanation of a common trick used in computer vision and text retrieval.
Bayesian Color Constancy with Non-Gaussian Models
(2003)
Document image decoding using iterated complete path search
(2001)
An Optimized Interaction Strategy for Bayesian Relevance Feedback
(1998)
Modeling user subjectivity in image libraries
(1996)
An Image Database Browser that Learns from User Interaction
(1996)
Interactive Learning using a "Society of Models"
(1997)
Vision Texture for Annotation
(1995)
Text retrieval
A Novel Click Model and Its Applications to Online Advertising
(2010)
Click Chain Model in Web Search
(2009)
Selection bias in the LETOR datasets
(2008)
SoftRank: Optimising Non-Smooth Ranking Metrics
(2008)
The Smoothed Dirichlet distribution: A new building block for generative topical models
(2007)
Novelty and Redundancy Detection in Adaptive Filtering
(2002)
Probabilistic modeling
Probabilistic Programming with Infer.NET
(2009)
Discriminative models, not discriminative training
(2005)
Building statistical models by visualization
(2003)
Conjugate Analysis of the Conway-Maxwell-Poisson Distribution
(2003)
A Useful Distribution for Fitting Discrete Data: Revival of the COM-Poisson
(2003)
Computing with the COM-Poisson distribution
(2003)
Judging significance from error bars
(2002)
Something everyone should know how to do, but probably doesn't.
Learning How to Learn is Learning With Point Sets
(1999)
The Dirichlet-tree distribution
(1999)
The next time you use a Dirichlet, consider a Dirichlet-tree instead.
From Hidden Markov Models to Linear Dynamical Systems
(1998)
Independence Diagrams
(1998)
A summary of Bayesian network notation.
Nuances of probability theory
(1998)
Old and New Matrix Algebra Useful for Statistics
(1997)
Optimization
Local Training and Belief Propagation
(2006)
Estimating a Gamma distribution
(2002)
A comparison of numerical optimizers for logistic regression
(2003)
Derives and compares eight methods, including iterative scaling.
Beyond Newton's method
(2000)
Custom approximations for fast optimization.
Estimating a Dirichlet distribution
(2000)
Optimization using Newton, modified Newton, and lower bounds.
Expectation-Maximization as lower bound maximization
(1998)
Last modified: Tue Apr 10 15:12:35 GMT 2007