Publications by Kevin Murphy

Some talks

"A unified, decision-theoretic perspective on ML: prediction, control, discovery, generation". This is an invited talk I gave at Princeton and NYU in May 2023.
"The 4 pillars of machine learning: prediction, decision, discovery, generation". This is an invited talk I gave at UBC (University of British Columbia) and SFU (Simon Fraser University) in Vancouver, February, 2023.
"From perception to prediction". This is a talk I gave at the Vector Institute in Toronto, January 2019. (Sound quality of video improves after first 10 minutes!)
"Generative models for vision and language". This is a short talk I gave at the "Deep learning: theory, algorithms and applications" workshop in Tokyo, March 2018.
"Towards machines that perceive and communicate". This is a talk given at UBC CS 50th annniversary, May 2018.
"Towards machines that perceive and communicate". This is a talk I gave at MIT CBMM (Center for Brains, Minds and Machines), May 2017.

Publications

141 peer reviewed publications (marked *).
30 non-peer reviewed publications (marked +).

2024

+ BONE: a unifying framework for Bayesian online learning in non-stationary environments
Gerardo Duran-Martin, Leandro Sánchez-Betancourt, Alexander Y. Shestopaloff, Kevin Murphy
Arxiv

+ Diffusion Model Predictive Control
Guangyao Zhou, Sivaramakrishnan Swaminathan, Rajkumar Vasudeva Raju, J. Swaroop Guntupalli, Wolfgang Lehrach, Joseph Ortiz, Antoine Dedieu, Miguel Lázaro-Gredilla, Kevin Murphy
Arxiv

* DMC-VB: A Benchmark for Representation Learning for Control with Visual Distractors
Joseph Ortiz, Antoine Dedieu, Wolfgang Lehrach, J Swaroop Guntupalli, Carter Wendelken, Ahmad Humayun, Sivaramakrishnan Swaminathan, Guangyao Zhou, Miguel Lazaro-Gredilla, Kevin Patrick Murphy
NeurIPS

* EM Distillation for One-step Diffusion Models
Sirui Xie, Zhisheng Xiao, Kevin Murphy, Tim Salimans, BenPoole, Ruiqi Gao
NeurIPS

* What type of inference is planning?
Miguel Lazaro-Gredilla, Li Yang Ku, Kevin P. Murphy, Dileep George
NeurIPS

* Bayesian Online Natural gradient
Matt Jones, Peter Chang, Kevin Murphy.
NeurIPS

+ Model Predictive Simulation Using Structured Graphical Models and Transformers
Xinghua Lou, Meet Dave, Shrinu Kushagra, Miguel Lazaro-Gredilla, Kevin Murphy
CVPR Workshop on Autonomous Driving

* Outlier-robust Kalman Filtering through Generalised Bayes
Gerardo Duran-Martin,Matias Altamirano, Alexander Y. Shestopaloff, Leandro Sanchez-Betancourt, Jeremias Knoblauch, Matt Jones, Francois-Xavier Briol, Kevin Murphy
ICML

* Model-based Policy Optimization under Approximate Bayesian Inference (Oral)
Chaoqi Wang , Yuxin Chen, Kevin Murphy.
AISTATS

* Don't Be Pessimistic Too Early: Look K Steps Ahead!
Chaoqi Wang , Yuxin Chen, Kevin Murphy.
AISTATS

2023

* Beyond Invariance: Test-Time Label-Shift Adaptation for Distributions with "Spurious" Correlations
Qingyao Sun, Kevin Murphy, Sayna Ebrahimi, Alexander D'Amour
NeurIPS'23.

* SPAE: Semantic Pyramid AutoEncoder for Multimodal Generation with Frozen LLMs
Lijun Yu, Yong Cheng, Zhiruo Wang, Vivek Kumar, Wolfgang Macherey, Yanping Huang, David A. Ross, Irfan Essa, Yonatan Bisk, Ming-Hsuan Yang, Kevin Murphy, Alexander G. Hauptmann, Lu Jiang
NeurIPS'23.

* Model-based Policy Optimization under Approximate Bayesian Inference
Chaoqi Wang, Yuxin Chen, Kevin Patrick Murphy
ICML Workshop on Learning, Control, and Dynamical Systems

* LoFi: Low-rank Bayesian filtering for online learning of neural networks
Peter Chang, Gerardo Duran-Martin, Alex Shestopaloff, Matt Jones, Kevin Murphy
CoLLAs (Second Conference on Lifelong Learning Agents)

* Muse: Text-To-Image Generation via Masked Generative Transformers
Huiwen Chang, Han Zhang, Jarred Barber, AJ Maschinot, Jose Lezama, Lu Jiang, Ming-Hsuan Yang, Kevin Murphy, William T. Freeman, Michael Rubinstein, Yuanzhen Li, Dilip Krishnan
ICML

2022

* On diagonal approximations to the extended Kalman filter for online training of Bayesian neural networks
Peter Chang, Kevin Murphy, Matt Jones.
ACML Workshop on Continual Lifelong Learning.

* Uncertainty Disentanglement with Non-stationary Heteroscedastic Gaussian Processes for Active Learning
Zeel B Patel, Nipun Batra, Kevin Murphy
NeurIPS Workshop on Gaussian Processes, Spatiotemporal Modeling, and Decision-making Systems

* Reliability benchmarks for image segmentation
E. Kelly Buchanan, Michael W Dusenberry, Jie Ren, Kevin Patrick Murphy, Balaji Lakshminarayanan, Dustin Tran
NeurIPS Workshop on Distribution Shift

* Language Model Cascades
David Dohan, Aitor Lewkowycz, Jacob Austin, Winnie Xu, Yuhuai Wu, David Bieber, Raphael Gontijo-Lopes, Henryk Michalewski, Rif A. Saurous, Jascha Sohl-Dickstein, Kevin Patrick Murphy, Charles Sutton
ICML'22 Workshop on Beyond Bayes

* Plex: Towards Reliability using Pretrained Large Model Extensions
Dustin Tran, Andreas Kirsch, Balaji Lakshminarayanan, Huiyi Hu, Du Phan, D. Sculley, Jasper Snoek, Jeremiah Zhe Liu, Jie Ren, Joost van Amersfoort, Kehang Han, E. Kelly Buchanan, Kevin Murphy, Mark Collier, Michael W Dusenberry, Neil Band, Nithum Thain, Rodolphe Jenatton, Tim G. J. Rudner, Yarin Gal, Zachary Nado, Zelda E Mariet, Zi Wang, Zoubin Ghahramani
ICML'22 Workshop on Pre-Training

* COVID-19 Open-Data: A global-scale spatially granular meta-dataset for coronavirus disease
Oscar Wahltinez, Aurora Cheung, Ruth Alcantara, Donny Cheung, Paula Le, Anthony Erlinger, Ofir Picazo Navarro, Mayank Daswani, Matt Lee, Kevin Murphy, and Michael Brenner
Nature Scientific Data
Code/data

*Efficient Online Bayesian Inference for Neural Bandits
Gerardo Duran-Martin, Aleyna Kara, Kevin Murphy
AISTATS 2022.
Code

+ Probabilistic Machine Learning: An Introduction
Kevin Murphy.
MIT Press, 2022.
Code

* Machine Learning on Graphs: A Model and Comprehensive Taxonomy
Ines Chami, Sami Abu-El-Haija, Bryan Perozzi, Christopher Re, Kevin Murphy
JMLR 2022.

2021

* Risk score learning for COVID-19 contact tracing apps
Kevin Murphy, Abhishek Kumar, Stelios Serghiou
Machine Learning for Health Care, 2021.
Code

* Uncertainty Baselines: Benchmarks for Uncertainty & Robustness in Deep Learning
Zachary Nado, Neil Band, Mark Collier, Josip Djolonga, Michael W. Dusenberry, Sebastian Farquhar, Angelos Filos, Marton Havasi, Rodolphe Jenatton, Ghassen Jerfel, Jeremiah Liu, Zelda Mariet, Jeremy Nixon, Shreyas Padhy, Jie Ren, Tim G. J. Rudner, Yeming Wen, Florian Wenzel, Kevin Murphy, D. Sculley, Balaji Lakshminarayanan, Jasper Snoek, Yarin Gal, Dustin Tran
NeurIPS Bayesian Deep Learning Workshop, 2021.

2020

* Collapsed Amortized Variational Inference for Switching Nonlinear Dynamical Systems
Zhe Dong, Bryan A. Seybold, Kevin P. Murphy, Hung H. Bui
ICML 2020
Code

* Population Based Optimization for Biological Sequence Design
Andreea Gane, Christof Angermueller, D. Sculley, David Belanger, David Martin Dohan, Kevin Patrick Murphy, Lucy Colwell, Zelda Mariet
ICML 2020

* Amortized Bayesian Optimization over Discrete Spaces
Yulia Rubanova, Davdid Dohan, Kevin Swersky, Kevin Murphy
UAI 2020

* Towards Differentiable Resampling
Michael Zhu, Kevin Murphy, Rico Jonschkowski
RSS workshop on "Action Representations for Learning in Continuous Control", 2020.

* A view of Estimation of Distribution Algorithms through the lens of Expectation-Maximization
David H. Brookes, Akosua Busia, Clara Fannjiang, Kevin Murphy, Jennifer Listgarten
GECCO 2020

* Model-based reinforcement learning for biological sequence design
Christof Angermueller, David Dohan, David Belanger, Ramya Deshpande, Kevin Murphy, Lucy Colwell
ICLR 2020

* The Garden of Forking Paths: Towards Multi-Future Trajectory Prediction
Junwei Liang, Lu Jiang, Kevin Murphy, Ting Yu, Alexander Hauptmann
CVPR 2020.
Project website, including videos.

* Regularized Autoencoders via Relaxed Injective Probability Flow
Abhishek Kumar, Ben Poole, Kevin Murphy
AISTATS 2020

2019

* Biological Sequence Design using Batched Bayesian Optimization
David Belanger, Suhani Vora, Zelda Mariet, Ramya Deshpande, David Dohan, Christof Angermueller, Kevin Murphy, Olivier Chapelle and Lucy Colwell
NIPS19 workshop on ML for the sciences

* Probing Uncertainty Estimates of Neural Processes
Aditya Grover, Dustin Tran, Rui Shu, Ben Poole and Kevin Murphy
NIPS19 Bayesian deep learning workshop.

+ Learning Video Representations using Contrastive Bidirectional Transformer
Chen Sun, Fabien Baradel, Kevin Murphy, Cordelia Schmid
Arxiv 2019

*Language as an Abstraction for Hierarchical Deep Reinforcement Learning
Yiding Jiang, Shixiang Gu, Kevin Murphy, Chelsea Finn
NIPS'19

* Unsupervised Learning of Object Structure and Dynamics from Videos
Matthias Minderer, Chen Sun, Ruben Villegas, Forrester Cole, Kevin Murphy, Honglak Lee
NIPS'19

* Floors are Flat: Leveraging Semantics for Real-Time Surface Normal Prediction
Steven Hickson, Karthik Raveendran, Alireza Fathi, Kevin Murphy, Irfan Essa
ICCV'19 workshop on "Geometry meets Deep Learning"

* VideoBERT: A Joint Model for Video and Language Representation Learning
Chen Sun, Austin Myers, Carl Vondrick, Kevin Murphy, Cordelia Schmid
ICCV'19

* NAS-Bench-101: Towards Reproducible Neural Architecture Search
Chris Ying, Aaron Klein, Esteban Real, Eric Christiansen, Kevin Murphy, Frank Hutter
ICML'19.

*Composing Text and Image for Image Retrieval - An Empirical Odyssey
Nam Vo, Lu Jiang, Chen Sun, Kevin Murphy
CVPR'19

* Diverse Generation for Multi-agent Sports Games
Raymond A. Yeh, Jonathan Huang, Alexander G. Schwing, Kevin Murphy
CVPR'19

*Relational Action Forecasting
Chen Sun, Abhinav Shrivastava, Carl Vondrick, Rahul Sukthankar, Kevin Murphy, Cordelia Schmid
CVPR'19

* Predicting the Present and Future States of Multi-agent Systems from Partially-observed Visual Data
Chen Sun, Per Karlsson, Jiajun Wu, Joshua B Tenenbaum, Kevin Murphy
ICLR'19

* Modeling Uncertainty with Hedged Instance Embeddings
Seong Joon Oh, Kevin P. Murphy, Jiyan Pan, Joseph Roth, Florian Schroff, Andrew C. Gallagher
ICLR'19

* Modeling Parts, Structure, and System Dynamics via Predictive Learning
Zhijian Liu, Jiajun Wu, Zhenjia Xu, Chen Sun, Kevin Murphy, William T. Freeman, Joshua B. Tenenbaum
ICLR'19

2018

* Towards Reproducible Neural Architecture and Hyperparameter Search
Aaron Klein, Eric Christiansen, Kevin Murphy, Frank Hutter
ICML'18 Workshop on Reproducible ML

* Progressive Neural Architecture Search
Chenxi Liu, Barret Zoph, Jonathon Shlens, Wei Hua, Li-Jia Li, Li Fei-Fei, Alan Yuille, Jonathan Huang, Kevin Murphy
ECCV'18.

* Tracking emerges by colorizing videos
Carl Vondrick, Abhinav Shrivastava, Alireza Fathi, Sergio Guadarrama, Kevin Murphy
ECCV'18.

* Actor-centric Relation Network
Chen Sun, Abhinav Shrivastava, Carl Vondrick, Kevin Murphy, Rahul Sukthankar, Cordelia Schmid.
ECCV'18.

* PersonLab: Person Pose Estimation and Instance Segmentation with a Bottom-Up, Part-Based, Geometric Embedding Model
George Papandreou, Tyler Zhu, Liang-Chieh Chen, Spyros Gidaris, Jonathan Tompson, Kevin Murphy
ECCV'18.

* Rethinking Spatiotemporal Feature Learning For Video Understanding
Saining Xie, Chen Sun, Jonathan Huang, Zhuowen Tu, Kevin Murphy.
ECCV'18.

* Generative Models of Visually Grounded Imagination
Ramakrishna Vedantam, Ian Fischer, Jonathan Huang, Kevin Murphy
ICLR'18.
Tensorflow code.

* Fixing a broken ELBO
Alexander A. Alemi, Ben Poole, Ian Fischer, Joshua V. Dillon, Rif A. Saurous, Kevin Murphy
ICML'18.

* XGAN: Unsupervised Image-to-Image Translation for Many-to-Many Mappings
Amelie Royer, Konstantinos Bousmalis, Stephan Gouws, Fred Bertsch, Inbar Mosseri, Forrester Cole, Kevin Murphy
ICML workshop on domain adaptation for visual understanding (DAVU'18).

2017

* PixColor: Pixel Recursive Colorization
Sergio Guadarrama, Ryan Dahl, David Bieber, Mohammad Norouzi, Jonathon Shlens, Kevin Murphy
BMVC'17.

* Attention-based Extraction of Structured Information from Street View Imagery
Zbigniew Wojna, Alex Gorban, Dar-Shyang Lee, Kevin Murphy, Qian Yu, Yeqing Li, Julian Ibarz
ICDAR'17.
Google Research Blog post.

* DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, Alan L. Yuille
IEEE PAMI, 2017.

* Deep Probabilistic Programming
Dustin Tran, Matthew D. Hoffman, Rif A. Saurous, Eugene Brevdo, Kevin Murphy, David M. Blei
ICLR'17.
Tensorflow code.

* Deep Variational Information Bottleneck
Alexander A. Alemi, Ian Fischer, Joshua V. Dillon, Kevin Murphy
ICLR'17.
Tensorflow Code.

+ Semantic Instance Segmentation via Deep Metric Learning
Alireza Fathi, Zbigniew Wojna, Vivek Rathod, Peng Wang, Hyun Oh Song, Sergio Guadarrama, Kevin P. Murphy
Arxiv'17 .

* Context-aware Captions from Context-agnostic Supervision
Ramakrishna Vedantam, Samy Bengio, Kevin Murphy, Devi Parikh, Gal Chechik
CVPR'17.

* Deep Metric Learning via Facility Location
Hyun Oh Song, Stefanie Jegelka, Vivek Rathod, Kevin Murphy
CVPR'17.

* Speed/accuracy trade-offs for modern convolutional object detectors
Jonathan Huang, Vivek Rathod, Chen Sun, Menglong Zhu, Anoop Korattikara, Alireza Fathi, Ian Fischer, Zbigniew Wojna, Yang Song, Sergio Guadarrama, Kevin Murphy
CVPR'17.
Tensorflow code. Google Research blog post. Google Cloud ML blog post. Hacker News.

*Towards Accurate Multi-person Pose Estimation in the Wild
George Papandreou, Tyler Zhu, Nori Kanazawa, Alexander Toshev, Jonathan Tompson, Chris Bregler, Kevin Murphy
CVPR'17.

* Optimization of image description metrics using policy gradient methods
Siqi Liu, Zhenhai Zhu, Ning Ye, Sergio Guadarrama, Kevin Murphy
ICCV'17.

2016

* Generation and Comprehension of Unambiguous Object Descriptions
Junhua Mao, Jonathan Huang, Alexander Toshev, Oana Camburu, Alan Yuille, Kevin Murphy
CVPR'16
Dataset. TechCrunch article.

* Detecting events and key actors in multi-person videos
Vignesh Ramanathan, Jonathan Huang, Sami Abu-El-Haija, Alexander Gorban, Kevin Murphy, Li Fei-Fei.
CVPR'16
Dataset. TechCrunch article.

* Semantic Image Segmentation with Task-Specific Edge Detection Using CNNs and a Discriminatively Trained Domain Transform
Liang-Chieh Chen, Jonathan T. Barron, George Papandreou, Kevin Murphy, Alan L. Yuille
CVPR'16

* Efficient inference in occlusion-aware generative models of images
Jonathan Huang, Kevin Murphy
ICLR'16 Workshop.

2015

* Im2Calories: towards an automated mobile vision food diary
Austin Myers, Nick Johnston, Vivek Rathod, Anoop Korattikara, Alex Gorban, Nathan Silberman, Sergio Guadarrama, George Papandreou, Jonathan Huang, Kevin Murphy.
ICCV'15
Reddit thread. Coverage in popular press: CNET, Fortune, The Guardian, and many many more!

*Bayesian Dark Knowledge
Anoop Korattikara, Vivek Rathod, Kevin Murphy, Max Welling
NIPS'15

* Modeling and Diagnosis of Structural Systems through Sparse Dynamic Graphical Models
Luke Bornn, Charles R Farrar, David Higdon, Kevin Murphy.
J. of Mechanical Systems and Signal Processing, 2015

* A Review of Relational Machine Learning for Knowledge Graphs: From Multi-Relational Link Prediction to Automated Knowledge Graph Construction
Maximilian Nickel, Kevin Murphy, Volker Tresp, Evginey Gabrilovich
Proc. IEEE, 2015

* What's Cookin'? Interpreting Cooking Videos using Text, Speech and Vision
Jon Malmaud, Jonathan Huang, Vivek Rathod, Nicholas Johnston, Andrew Rabinovich, Kevin Murphy
NAACL'15

*Probabilistic Label Relation Graphs with Ising Models
Nan Ding, Jia Deng, Kevin Murphy, Hartmut Neven
ICCV 2015

* TimeMachine: Timeline Generation for Knowledge-Base Entities
Tim Althoff, Xin Luna Dong, Kevin Murphy, Safa Alai, Van Dang, Wei Zhang
KDD'15

* Knowledge-Based Trust: Estimating the Trustworthiness of Web Sources
Xin Luna Dong, Evgeniy Gabrilovich, Kevin Murphy, Van Dang, Wilko Horn, Camillo Lugaresi, Shaohua Sun, Wei Zhang
VLDB'15.
Popular press coverage: CACM, New Scientist, The Independent, Washington Post, Fox News, Salon, other US media outlets

* Weakly and semi-supervised learning of a DCNN for semantic image segmentation
George Papandreou, Liang-Chieh Chen, Kevin Murphy, Alan L. Yuille
ICCV 2015

* Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs
Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, Alan L. Yuille
ICLR'15

2014

* Probabilistic models for collective entity resoluion between knowledge graphs
Jay Pujara, Kevin Murphy, Luna Dong, Curtis Janssen
Bay Area Machine Learning workshop 2014
(Extended abstract)

*Canonicalizing Open Knowledge Bases
Luis Galarraga, Geremy Heitz, Kevin Murphy, Fabian Suchanek
CIKM 2014

*Large-Scale Object Classification using Label Relation Graphs
Jia Deng, Nan Ding, Yangqing Jia, Andrea Frome, Kevin Murphy, Samy Bengio, Yuan Li, Hartmut Neven, Hartwig Adam
ECCV 2014 (Best paper award)

* Cooking with semantics
Jon Malmaud, Earl Wagner, Nancy Chang, Kevin Murphy.
ACL'14 Semantic Parsing Workshop

* Knowledge Vault: A Web-Scale Approach to Probabilistic Knowledge Fusion
Xin Luna Dong, Evgeniy Gabrilovich, Geremy Heitz, Wilko Horn, Ni Lao, Kevin Murphy, Thomas Strohmann, Shaohua Sun, Wei Zhang
KDD'14.

* From Data Fusion to Knowledge Fusion
Xin Luna Dong, Evgeniy Gabrilovich, Geremy Heitz, Wilko Horn, Kevin Murphy, Shaohua Sun, Wei Zhang
VLDB'14.

* Knowledge Base Completion via Search-Based Question Answering
Robert West, Evgeniy Gabrilovich, Kevin Murphy, Shaohua Sun, Rahul Gupta, Dekang Lin
WWW'14.

*Identifying Players in Broadcast Sports Videos using Conditional Random Fields
Wei-Lwun Lu, Jo-Anne Ting, Kevin P. Murphy, and James J. Little
CVPR 2011.
More info on the project
For a more recent version, see the PAMI 2012 paper.

*Multiscale Conditional Random Fields for Semi-supervised Labeling and Classification
David Duvenaud, Ben Marlin, Kevin Murphy.
Canadian Conf. on Computer and Robot Vision (CRV) 2011

2010

*Variational bounds for mixed-data factor analysis
M. E. Khan, B. Marlin, G. Bouchard, K. Murphy
NIPS 2010.

* Pairwise network mechanisms in the host signaling response to coxsackievirus B3 infection
Farshid S. Garmaroudia, David Marchant, Xiaoning Si, Abbas Khalili, Ali Bashashati, Brian W. Wong, Aline Tabet, Raymond T. Ng, Kevin Murphy, Honglin Luo, Kevin A. Janes, Bruce M. McManus.
Proc. Natl. Acad. Sciences, 107(39): 17053-17058, 2010

* Computational approaches for RNA energy parameter estimation
M. Andronescu and A. Condon and H. Hoos and K. Murphy and D. Mathews
RNA Journal, 16(12):2304-2118, 2010

*Convex Structure Learning in Log-Linear Models: Beyond Pairwise Potentials
M. Schmidt, K. Murphy
AI/Stats 2010.

*Time-Bounded Sequential Parameter Optimization
Frank Hutter, Holger Hoos, Kevin Murphy, Kevin Leyton-Brown
Learning and Intelligent Optimization - LION4 2010.
Runner up for best paper award.

+ Book Review of "Probabilistic graphical models" by Koller and Friedman
Kevin Murphy
Artificial Intelligence Journal, 174(2): 145--146, 2010.
(Invited submission).

*SNVMix: predicting single nucleotide variants from next generation sequencing of tumors
R. Goya , M. Sun , R. Morin , G. Leung , G. Ha , K. Wieg, , J. Senz , A. Crisan , M. Marra , M. Hirst , D. Huntsman , K. Murphy , S. Aparicio , S. Shah
Bioinformatics, 2010

+Using the forest to see the trees: object recognition in context
A. Torralba, K. Murphy, W. Freeman,
Communications of the ACM, Research Highlights, 53(3): 107-114, 2010.
(Invited submission).

2009

* Accelerating Bayesian Structural Inference for Non-Decomposable Gaussian Graphical Models
Baback Moghaddam, Ben Marlin, Emtiyaz Khan, Kevin Murphy.
NIPS 2009

* Causal learning without DAGs
David Duvenaud, Daniel Eaton, Kevin Murphy, Mark Schmidt.
JMLR W&CP 2009.
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)

2008

*Structure Learning in Random Fields for Heart Motion Abnormality Detection,
Mark Schmidt, Kevin Murphy, Glenn Fung, Romer Rosales.
CVPR 2008. Appendix. Software.

* 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.

2007

+ Software for graphical models: a review.
Kevin Murphy.
ISBA (Intl. Soc. for Bayesian Analysis) Bulletin, 14(4), pages 13-15, December 2007.
(Invited submission).

*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)

+Conjugate Bayesian analysis of the univariate Gaussian: a tutorial
Kevin Murphy, September 2007.

2006

* Integrating copy number polymorphisms into array CGH analysis using a robust HMM
S Shah, X Xuang, R DeLeeuw, M Khojasteh, W Lam, R Ng, K Murphy
Bioinformatics, 22(14):e431-e439, July 2006. Software.

*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.

2005

+ Object detection and localization using local and global features
Kevin Murphy, Antonio Torralba, Daniel Eaton, William Freeman
Appears in Towards Category-Level Object Recognition
LNCS Vol. 4170, 2006, Editors J. Ponce, M. Hebert, C. Schmid, A. Zisserman.
(Invited submission).

+ 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).

2004

* Contextual Models for Object Detection using Boosted Random Fields
Antonio Torralba, Kevin Murphy and William Freeman
NIPS'04.

* 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)

2003

* "Using the Forest to See the Trees:A Graphical Model Relating Features, Objects and Scenes"
Kevin Murphy, Antonio Torralba, William Freeman
NIPS'03 (Neural Info. Processing Systems)
More information about this project (including movies) is available.

* 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.

+ Fitting a constrained conditional linear Gaussian distribution
Kevin Murphy. October 1998, updated January 2003.

2002

* Dynamic Bayesian Networks for Audio-Visual Speech Recognition
A. Nefian, L. Liang, X. Pi, X. Liu and K. Murphy
EURASIP, Journal of Applied Signal Processing, 11:1-15, 2002

* 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.
The Encyclopedia of Cognitive Science
L. Nadel et al. (eds), Nature Macmillan, 2002.
(Invited submission).

+ Hidden semi-Markov models (segment models)
Kevin Murphy. November 2002.

+ Dynamic Bayesian Networks.
Kevin Murphy. November 2002.

+ Pearl's algorithm for vector Gaussian Bayes Nets
Kevin Murphy. March 2002.

2001

* Linear Time Inference in Hierarchical HMMs
Kevin Murphy and Mark Paskin.
NIPS '01 (Neural Info. Proc. Systems).

* 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).
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.

+ An introduction to graphical models
Kevin Murphy. May 2001.

+ Active learning of causal Bayes net structure
Kevin Murphy. March 2001.

2000

* Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks
Arnaud Doucet, Nando de Freitas, Kevin Murphy and Stuart Russell.
UAI '00 (Uncertainty in AI).

+ 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.

1999

* Bayesian Map Learning in Dynamic Environments
Kevin Murphy.
NIPS '99 (Neural Info. Proc. Systems).

* 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).) Modelling Gene Expression Data using Dynamic Bayesian Networks
Kevin Murphy and Saira Mian. 1999.

Pearl's algorithm for multiplexer nodes
Kevin Murphy. 1999.

1998

* Learning the Structure of Dynamic Probabilistic Networks
Nir Friedman, Kevin Murphy, and Stuart Russell.
UAI '98 (Uncertainty in AI).

+ 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.

1997

* Space-efficient Inference in Dynamic Probabilistic Networks
John Binder, Kevin Murphy, and Stuart Russell.
IJCAI '97 (Intl. Joint Conf. on AI).

1996

+ 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

1995

* Automata-Theoretic Models of Mutation and Alignment
David Searls and Kevin Murphy.
ISMB '95 (Intelligent Systems For Molecular Biology).