Preprints & Technical Reports

  • Prediction-Constrained Training for Semi-Supervised Mixture and Topic Models. M. Hughes, L. Weiner, G. Hope, T. McCoy Jr., R. Perlis, E. Sudderth, and F. Doshi-Velez, arXiv:1707.07341, stat.ML, July 2017. arXiv
  • Fast Learning of Clusters and Topics via Sparse Posteriors. M. Hughes and E. Sudderth, arXiv:1609.07521, stat.ML, September 2016. arXiv

2017

  • Cascaded Scene Flow Prediction using Semantic Segmentation. Z. Ren, D. Sun, J. Kautz, and E. Sudderth, International Conference on 3D Vision, 2017. paper · arXiv
  • From Patches to Images: A Nonparametric Generative Model. G. Ji, M. Hughes, and E. Sudderth, International Conference on Machine Learning, Aug. 2017. paper · supplement
  • Refinery: An Open Source Topic Modeling Web Platform. D. Kim, B. Swanson, M. Hughes, and E. Sudderth, Journal of Machine Learning Research, vol. 18, Mar. 2017. paper · jmlr · code · video demo

2016

  • Three-Dimensional Object Detection and Layout Prediction using Clouds of Oriented Gradients. Z. Ren and E. Sudderth, IEEE Conference on Computer Vision & Pattern Recognition, June 2016. paper · supplement

2015

  • Scalable Adaptation of State Complexity for Nonparametric Hidden Markov Models. M. Hughes, W. Stephenson, and E. Sudderth, Neural Information Processing Systems, Dec. 2015. paper · supplement
  • Proteins, Particles, and Pseudo-Max-Marginals: A Submodular Approach. J. Pacheco and E. Sudderth, International Conference on Machine Learning, July 2015. paper
  • Layered RGBD Scene Flow Estimation. D. Sun, E. Sudderth, and H. Pfister, IEEE Conference on Computer Vision & Pattern Recognition, June 2015. paper
  • Reliable and Scalable Variational Inference for the Hierarchical Dirichlet Process. M. Hughes, D. Kim, and E. Sudderth, Artificial Intelligence & Statistics, May 2015. paper
  • A Spectral Clustering Search Algorithm for Predicting Shallow Landslide Size and Location. D. Bellugi, D. Milledge, W. Dietrich, J. McKean, J. Perron, E. Sudderth, and B. Kazian, JGR: Earth Surface, vol. 120, 2015. paper

2014

  • Joint Modeling of Multiple Time Series via the Beta Process with Application to Motion Capture Segmentation. E. Fox, M. Hughes, E. Sudderth, and M. Jordan, Annals of Applied Statistics, vol. 8(3), 2014. paper
  • Nonparametric Clustering with Distance Dependent Hierarchies. S. Ghosh, M. Raptis, L. Sigal, and E. Sudderth, Uncertainty in Artificial Intelligence, July 2014. paper
  • Preserving Modes and Messages via Diverse Particle Selection. J. Pacheco, S. Zuffi, M. Black, and E. Sudderth, International Conference on Machine Learning, June 2014. paper
  • Quantifying Aphid Behavioral Responses to Environmental Change. E. A. Sudderth and E. B. Sudderth, Entomologia Experimentalis et Applicata, vol. 150, 2014. paper

2013

  • Memoized Online Variational Inference for Dirichlet Process Mixture Models M. Hughes and E. Sudderth Neural Information Processing Systems, Dec. 2013. paper
  • Efficient Online Inference for Bayesian Nonparametric Relational Models D. Kim, P. Gopalan, D. Blei, and E. Sudderth Neural Information Processing Systems, Dec. 2013. paper
  • A Fully-Connected Layered Model of Foreground and Background Flow D. Sun, J. Wulff, E. Sudderth, H. Pfister, and M. Black IEEE Conference on Computer Vision & Pattern Recognition, June 2013. paper
  • NET-VISA: Network Processing Vertically Integrated Seismic Analysis N. Arora, S. Russell, and E. Sudderth Bulletin of the Seismological Society of America, vol. 103(2a), Apr. 2013. paper

2012

  • Truly Nonparametric Online Variational Inference for Hierarchical Dirichlet Processes M. Bryant and E. Sudderth Neural Information Processing Systems, Dec. 2012. paper
  • From Deformations to Parts: Motion-based Segmentation of 3D Objects S. Ghosh, E. Sudderth, M. Loper, and M. Black Neural Information Processing Systems, Dec. 2012. paper
  • Effective Split-Merge Monte Carlo Methods for Nonparametric Models of Sequential Data M. Hughes, E. Fox, and E. Sudderth Neural Information Processing Systems, Dec. 2012. paper
  • Minimization of Continuous Bethe Approximations: A Positive Variation J. Pacheco and E. Sudderth Neural Information Processing Systems, Dec. 2012. paper
  • Improved Variational Inference for Tracking in Clutter J. Pacheco and E. Sudderth IEEE Statistical Signal Processing Workshop, Aug. 2012. paper
  • The Nonparametric Metadata Dependent Relational Model D. Kim, M. Hughes, and E. Sudderth International Conference on Machine Learning, June 2012. paper
  • Layered Segmentation and Optical Flow Estimation Over Time D. Sun, E. Sudderth, and M. Black IEEE Conference on Computer Vision & Pattern Recognition, June 2012. paper
  • Nonparametric Learning for Layered Segmentation of Natural Images S. Ghosh and E. Sudderth IEEE Conference on Computer Vision & Pattern Recognition, June 2012. paper
  • Nonparametric Discovery of Activity Patterns from Video Collections M. Hughes and E. Sudderth CVPR Workshop on Perceptual Organization in Computer Vision. paper

2011

  • The Doubly Correlated Nonparametric Topic Model D. Kim and E. Sudderth Neural Information Processing Systems, Dec. 2011. paper
  • Spatial Distance Dependent Chinese Restaurant Processes for Image Segmentation S. Ghosh, A. Ungureanu, E. Sudderth, and D. Blei Neural Information Processing Systems, Dec. 2011. paper
  • A Sticky HDP-HMM with Application to Speaker Diarization E. Fox, E. Sudderth, M. Jordan, and A. Willsky Annals of Applied Statistics, vol. 5(2A), 2011. paper · arXiv
  • Bayesian Nonparametric Inference of Switching Dynamic Linear Models E. Fox, E. Sudderth, M. Jordan, and A. Willsky IEEE Transactions on Signal Processing, vol. 59(4), Apr. 2011. paper
  • Global Seismic Monitoring: A Bayesian Approach N. Arora, S. Russell, P. Kidwell, and E. Sudderth AAAI Conference on Artificial Intelligence, Nectar track, 2011. paper

2010

  • Layered Image Motion with Explicit Occlusions, Temporal Consistency, and Depth Ordering D. Sun, E. Sudderth, and M. Black Neural Information Processing Systems, Dec. 2010. paper
  • Global Seismic Monitoring as Probabilistic Inference N. Arora, S. Russell, P. Kidwell, and E. Sudderth Neural Information Processing Systems, Dec. 2010. paper
  • Bayesian Nonparametric Learning of Markov Switching Processes E. Fox, E. Sudderth, M. Jordan, and A. Willsky IEEE Signal Processing Magazine, vol. 27(6), Nov. 2010. paper
  • Nonparametric Belief Propagation E. Sudderth, A. Ihler, M. Isard, W. Freeman, and A. Willsky Communications of the ACM, vol. 53(10), Oct. 2010. paper
  • Gibbs Sampling in Open-Universe Stochastic Languages N. Arora, R. de Salvo Braz, E. Sudderth, and S. Russell Uncertainty in Artificial Intelligence, July 2010. paper

2009

  • Sharing Features among Dynamical Systems with Beta Processes E. Fox, E. Sudderth, M. Jordan, and A. Willsky Neural Information Processing Systems, Dec. 2009. paper
  • Nonparametric Belief Propagation for Distributed Tracking of Robot Networks with Noisy Inter-Distance Measurements J. Schiff, E. Sudderth, and K. Goldberg IEEE International Conference on Intelligent Robots and Systems, Oct. 2009. paper
  • Nonparametric Bayesian Identification of Jump Systems with Sparse Dependencies E. Fox, E. Sudderth, M. Jordan, and A. Willsky IFAC Symposium on System Identification, July 2009. paper

2008

  • Shared Segmentation of Natural Scenes Using Dependent Pitman-Yor Processes E. Sudderth and M. Jordan Neural Information Processing Systems, Dec. 2008. paper · slides
  • Nonparametric Bayesian Learning of Switching Linear Dynamical Systems E. Fox, E. Sudderth, M. Jordan, and A. Willsky Neural Information Processing Systems, Dec. 2008. paper
  • An HDP-HMM for Systems with State Persistence E. Fox, E. Sudderth, M. Jordan, and A. Willsky International Conference on Machine Learning, July 2008. paper
  • Describing Visual Scenes Using Transformed Objects and Parts E. Sudderth, A. Torralba, W. Freeman, and A. Willsky International Journal of Computer Vision, vol. 77, Mar. 2008. paper
  • Signal and Image Processing with Belief Propagation E. Sudderth and W. Freeman IEEE Signal Processing Magazine, DSP Applications Column, Mar. 2008. paper

2007

  • Loop Series and Bethe Variational Bounds in Attractive Graphical Models E. Sudderth, M. Wainwright, and A. Willsky Neural Information Processing Systems, Dec. 2007. paper
  • Learning Multiscale Representations of Natural Scenes Using Dirichlet Processes J. Kivinen, E. Sudderth, and M. Jordan IEEE International Conference on Computer Vision, Oct. 2007. paper
  • Image Denoising with Nonparametric Hidden Markov Trees J. Kivinen, E. Sudderth, and M. Jordan IEEE International Conference on Image Processing, Sep. 2007. paper
  • Hierarchical Dirichlet Processes for Tracking Maneuvering Targets E. Fox, E. Sudderth, and A. Willsky International Conference on Information Fusion, July 2007. paper

2006

  • Depth from Familiar Objects: A Hierarchical Model for 3D Scenes E. Sudderth, A. Torralba, W. Freeman, and A. Willsky IEEE Conference on Computer Vision & Pattern Recognition, June 2006. paper

2005

  • Describing Visual Scenes using Transformed Dirichlet Processes E. Sudderth, A. Torralba, W. Freeman, and A. Willsky Neural Information Processing Systems, Dec. 2005. paper
  • Learning Hierarchical Models of Scenes, Objects, and Parts E. Sudderth, A. Torralba, W. Freeman, and A. Willsky International Conference on Computer Vision, Oct. 2005. paper

2004

  • Distributed Occlusion Reasoning for Tracking with Nonparametric Belief Propagation E. Sudderth, M. Mandel, W. Freeman, and A. Willsky Neural Information Processing Systems, Dec. 2004. paper
  • Embedded Trees: Estimation of Gaussian Processes on Graphs with Cycles E. Sudderth, M. Wainwright, and A. Willsky IEEE Transactions on Signal Processing, vol. 52(11), Nov. 2004. paper
  • Visual Hand Tracking Using Nonparametric Belief Propagation E. Sudderth, M. Mandel, W. Freeman, and A. Willsky Workshop on Generative Model Based Vision, CVPR, June 2004. paper

2003

  • Efficient Multiscale Sampling from Products of Gaussian Mixtures A. Ihler, E. Sudderth, W. Freeman, and A. Willsky Neural Information Processing Systems, Dec. 2003. paper
  • Nonparametric Belief Propagation E. Sudderth, A. Ihler, W. Freeman, and A. Willsky IEEE Conference on Computer Vision & Pattern Recognition, June 2003. paper

2002 & earlier

  • Statistical and Information-Theoretic Methods for Self-Organization and Fusion of Multimodal, Networked Sensors J. Fisher III, M. Wainwright, E. Sudderth, and A. Willsky Int. Journal of High Performance Computing Applications, vol. 16(3), Fall 2002. paper
  • Projection Algebra Analysis of Error-Correcting Codes J. Yedidia, E. Sudderth, and J-P. Bouchaud Allerton Conference on Communication, Control, and Computing, Oct. 2001. paper
  • Tree-Based Modeling and Estimation of Gaussian Processes on Graphs with Cycles M. Wainwright, E. Sudderth, and A. Willsky Neural Information Processing Systems, Dec. 2000. paper

Theses

  • Graphical Models for Visual Object Recognition and Tracking E. B. Sudderth Doctoral Thesis, Massachusetts Institute of Technology, May 2006. thesis
  • Embedded Trees: Estimation of Gaussian Processes on Graph with Cycles E. B. Sudderth Masters Thesis, Massachusetts Institute of Technology, Feb. 2002. thesis
  • A Kinematic Model Compiler for the Estimation of Articulated Motion from Video Sequences E. B. Sudderth Senior Honors Thesis, University of California at San Diego, April 1999. thesis

Selected Talks

  • Reliable Variational Learning for Hierarchical Dirichlet Processes E. Sudderth, M. Hughes, and D. Kim NIPS Workshop on Advances in Variational Inference, December 2014. slides
  • Reliable Variational Learning for Hierarchical Dirichlet Processes E. Sudderth, M. Hughes, D. Kim, P. Gopalan, and D. Blei International Society for Bayesian Analysis World Meeting, July 2014. slides
  • Toward Reliable Bayesian Nonparametric Learning E. Sudderth, D. Wei, M. Bryant, M. Hughes, and E. Fox NIPS Workshop on Bayesian Nonparametric Models for Reliable Planning and Decision-Making Under Uncertainty, Dec. 2012. slides
  • Spatial Bayesian Nonparametrics for Natural Image Segmentation E. Sudderth, M. Jordan, and S. Ghosh NIPS Workshop on Bayesian Nonparametrics: Hope or Hype? Dec. 2011. slides
  • Representation in Low-Level Visual Learning E. Sudderth NSF Workshop on Frontiers in Computer Vision, Aug. 2011. slides
  • Visual Learning via Topics, Transformations, and Trees E. Sudderth NIPS Workshop on Transfer Learning by Learning Rich Generative Models, Dec. 2010. slides
  • Loop Series and Bethe Variational Bounds in Attractive Graphical Models E. Sudderth, M. Wainwright, and A. Willsky Allerton Conference on Communication, Control, and Computing, Oct. 2007. slides

© 2017 Erik B. Sudderth · lastname@uci.edu