Accelerating Bayesian Structural Inference for
Non-Decomposable Gaussian Graphical Models
Baback Moghaddam, Ben Marlin, Emtiyaz Khan, Kevin Murphy.
NIPS 2009, to appear.
Using the forest to see the trees: object recognition in
context
A. Torralba, K. Murphy, W. Freeman,
{\em Comm. of the ACM}, Research Highlights,
2009, to appear.
(Invited submission, not peer reviewed)
Causal learning
without DAGs
David Duvenaud, Daniel Eaton, Kevin Murphy, Mark Schmidt.
JMLR W&CP 2009, to appear.
Software
Group Sparse Priors for Covariance Estimation
Ben Marlin, Mark Schmidt, and Kevin Murphy
UAI 2009
Modeling Discrete Interventional Data using
Directed Cyclic Graphical Models
Mark Schmidt, Kevin Murphy
UAI 2009
Sparse Gaussian Graphical Models with Unknown Block Structure
Ben Marlin and Kevin Murphy
ICML 2009
Model based clustering of array CGH data
Sohrab Shah, K-John Cheung, Nathalie Johnson,
Randy Gascoyne, Douglas Horsman, Raymond Ng, Kevin Murphy.
Bioinformatics
2009, 25(12):i30-i38.
An Experimental Investigation of Model-Based Parameter
Optimisation: SPO and Beyond
Frank Hutter, Kevin Leyton-Brown, Kevin Murphy.
Gecco 2009.
Sequential Model-Based Parameter Optimization: an Experimental
Investigation of Automated and Interactive Approaches
F. Hutter, T Bartz-Beielstein, H. Hoos, K. Leyton-Brown, K. Murphy
in Empirical Methods for the Analysis of Optimization
Algorithms, 2009.
A Hybrid Conditional Random Field for
estimating the underlying ground surface
from airborne LiDAR data
Wei-Lwun Lu, Kevin Murphy, James J. Little, Alla Sheffer, Hongbo Fu.
IEEE Trans. on Geoscience and Remote Sensing, 2009,
47(8):2913--2922.
Optimizing Costly Functions with Simple Constraints: A
Limited-Memory Projected Quasi-Newton Algorithm
Mark Schmidt, Ewout van den Berg, Michael Friedlander, Kevin Murphy
AI/Stats 2009
(Best paper award)
Genome-wide profiling of follicular lymphoma by array comparative
genomic hybridization reveals prognostically significant DNA copy number
imbalances
K-J. Cheung,
S. Shah,
C. Steidl,
N. Johnson,
T. Relander,
A. Telenius,
B. Lai,
K. Murphy,
W. Lam,
A. Al-Tourah,
J. Connors,
R. Ng,
R. Gascoyne,
D. Horsman.
Blood (J. of the Am. Soc. of Hematology), 2008.
LabelMe: a database and web-based tool for image annotation
Bryan Russell, Antonio Torralba, Kevin Murphy and William Freeman
Intl. J. Computer Vision (special issue on vision and
learning), 77(1-3): 157--173, 2008.
Software.
Software for graphical models: a review.
Kevin Murphy.
ISBA (Intl. Soc. for Bayesian Analysis) Bulletin, 14(4), pages 13-15, December
2007.
(Invited submission, not peer reviewed.)
Bayesian structure learning using
dynamic programming and MCMC
Daniel Eaton and Kevin Murphy
UAI 2007.
Software
Modeling changing dependency structure
in multivariate time series
Xiang Xuan and Kevin Murphy.
Intl. conf on machine learning (ICML), 2007.
Learning Graphical Model Structure using
L1-Regularization Paths
M Schmidt,
A Niculescu-Mizil, K Murphy.
AAAI'07.
Software
Efficient parameter estimation for
RNA secondary structure prediction
M Andronescu, A Condon, H Hoos, D Mathews, K Murphy.
Bioinformatics 2007
Modeling recurrent DNA copy number alterations in array
CGH data
S Shah, W Lam, R Ng, K Murphy.
Bioinformatics 2007.
Software.
Exact Bayesian structure learning from uncertain interventions
Daniel Eaton and Kevin Murphy.
AI & Statistics, 2007.
Software
Sharing visual features for multiclass and multiview object
detection
Antonio Torralba, Kevin Murphy and William Freeman
IEEE PAMI, 29(5), May 2007
Figure-ground segmentation using a hierarchical conditional
random field
Jordan Reynolds and Kevin Murphy.
Fourth Canadian Conference on Computer and Robot Vision (CRV 2007)
A non-myopic approach to visual
search
Julia Vogel and Kevin Murphy.
Fourth Canadian Conference on Computer and Robot Vision (CRV 2007)
Accelerated Training of Conditional Random Fields
with Stochastic Meta-Descent
S Vishwanathan,
N. Schraudolph,
M. Schmidt,
K. Murphy
ICML'06 (Intl Conf on Machine Learning)
Software.
Shared features for multiclass object detection
Antonio Torralba, Kevin Murphy, William Freeman
Appears in
Towards Category-Level Object Recognition
LNCS Vol. 4170, 2006,
Editors J. Ponce, M. Hebert, C. Schmid, A. Zisserman.
(Invited submission, not peer reviewed)
Sharing features: efficient boosting procedures for multiclass object
detection
Antonio Torralba, Kevin Murphy and William Freeman
CVPR'04 (Computer Vision and Pattern Recognition).
[Best poster award]
Representing hierarchical POMDPs as DBNs for multi-scale robot localization
Georgios Theocharous, Kevin Murphy, Leslie Kaelbling
ICRA'04 (Intl. Conf. on Robotics and Automation)
Context-based vision system for place and object recognition
Antonio Torralba, Kevin Murphy, William Freeman, Mark Rubin
ICCV'03 (Intl. Conf. on Computer Vision)
More information
about this project (including movies) is available.
A Coupled HMM for Audio-Visual Speech Recognition
A. Nefian, L. Liang, X. Pi, L. Xiaoxiang, C. Mao and K. Murphy
ICASSP '02 (IEEE Int'l Conf on Acoustics, Speech and Signal Proc.),
2:2013--2016.
Learning Markov Processes
Kevin Murphy.
Invited contribution to The Encyclopedia of Cognitive Science
L. Nadel et al. (eds), Nature Macmillan, 2002.
[Un-refereed]
The Factored Frontier Algorithm for Approximate Inference in DBNs
Kevin Murphy and Yair Weiss.
UAI '01 (Uncertainty in AI).
The Bayes Net Toolbox for Matlab
Kevin Murphy.
Computing Science and Statistics, vol 33, 2001.
(Invited submission, not peer reviewed)
The software is
available.
Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks
Kevin Murphy and Stuart Russell.
Appears in Sequential Monte Carlo Methods in Practice
A. Doucet, N. de Freitas and N.J. Gordon (eds),
Springer-Verlag, 2001.
Loopy-belief Propagation for Approximate Inference: An Empirical Study
Kevin Murphy, Yair Weiss, and Michael Jordan.
UAI '99 (Uncertainty in AI).
A Variational Approximation for Bayesian Networks with Discrete and Continuous Latent Variables
Kevin Murphy.
UAI '99 (Uncertainty in AI).
A Dynamic Bayesian Network Approach to Figure Tracking Using Learned Dynamic Models
Vladimir Pavlovic, James Rehg, Tat-Jen Cham, and Kevin Murphy.
ICCV '99 (Int'l Conf. on Computer Vision)
Vision-Based Speaker Detection Using Bayesian Networks
James Rehg, Kevin Murphy, and Paul Fieguth.
CVPR '99 (Computer Vision and Pattern Recognition).
(An earlier version of this work appeared in
PUI '98 (Perceptual User Interfaces).)
Exact inference in graphical models
presented at AAAI 2004 tutorial
Approximatte inference in graphical models
presented at AAAI 2004 tutorial
Graphical models and BNT
presented at the Mathworks, May 2003
An introduction to machine
learning and graphical models,
presented at the Intel workshop on "Machine learning for the life
sciences", Fall 2003
Tutorial on DBNs,
presented at the MIT AI Lab, November 2002
A review of methods for visual object
detection
Kevin Murphy, May 2005.
Proposed design for gR, a graphical
models toolkit for R
Kevin Murphy, September 2003.
Fitting a constrained conditional linear Gaussian distribution
Kevin Murphy. October 1998, updated January 2003.
Hidden semi-Markov models (segment models)
Kevin Murphy. November 2002.
Dynamic Bayesian Networks (Draft)
To appear in
Probabilistic Graphical Models, Michael Jordan.
Kevin Murphy. November 2002.
Tutorial on DBNs (slides)
Kevin Murphy. November 2002.
Representing Hierarchical POMDPs as DBNs, with Applications to Mobile
Robot Navigation
Kevin Murphy. November 2002.
This contains some more details than the ICML03 paper above.
Fast manipulation of multi-dimensional arrays in Matlab
Kevin Murphy. September 2002.
Pearl's algorithm for vector Gaussian Bayes Nets
Kevin Murphy. March 2002.
Hierarchical HMMs
Kevin Murphy. November 2001.
This is an extended version of my NIPS'01 paper.
See my thesis (chapter 2) for more information.
Applying the Junction Tree Algorithm to Variable-Length DBNs
Kevin Murphy. October 2001.
See my thesis (chapter 3) for a newer approach.
From Belief Propagation to Expectation Propagation
Kevin Murphy. September 2001.
See my thesis (appendix B) for related material.
Embedded graphical models
Kevin Murphy and Ara Nefian. June 2001.
Intel Research Technical Report.
An introduction to graphical models
Kevin Murphy. May 2001.
Active learning of causal Bayes net structure
Kevin Murphy. March 2001.
Learning Bayes net structure from sparse data sets
Kevin Murphy. February 2001.
See my thesis (appendix C) for more material.
A Survey of POMDP Solution Techniques
Kevin Murphy. September 2000.
Modeling Freeway Traffic using Coupled HMMs
Jaimyoung Kwon and Kevin Murphy. May 2000.
MCMC for Conditionally Linear Gaussian State-Space Models
Kevin Murphy. 2000.
Modelling Gene Expression Data using Dynamic Bayesian Networks
Kevin Murphy and Saira Mian. 1999.
Pearl's algorithm for multiplexer nodes
Kevin Murphy. 1999.
Filtering and Smoothing in Linear Dynamical Systems using the Junction Tree Algorithm
Kevin Murphy. 1998.
Learning Switching Kalman Filter Models
Kevin Murphy.
Compaq Cambridge Research Lab Tech Report 98-10, 1998.
Inference and learning in hybrid Bayesian networks
Kevin Murphy.
U.C. Berkeley Technical Report CSD-98-990, 1998.
Optimal Alignments in Linear Space using Automaton-Derived Cost Functions
Kevin Murphy. 1996.
Learning Finite Automata,
Kevin Murphy.
Santa Fe Institute Technical Report 96-04-017, 1996