Search Machine Learning Repository:
All publications by Erik B. Sudderth
authors venues years



Truly Nonparametric Online Variational Inference for Hierarchical Dirichlet Processes
Michael Bryant and Erik B. Sudderth
Advances in Neural Information Processing Systems 25, 2012


Minimization of Continuous Bethe Approximations: A Positive Variation
Jason Pacheco and Erik B. Sudderth
Advances in Neural Information Processing Systems 25, 2012


From Deformations to Parts: Motion-based Segmentation of 3D Objects
Soumya Ghosh, Matthew Loper, Erik B. Sudderth and Michael J. Black
Advances in Neural Information Processing Systems 25, 2012


Effective Split-Merge Monte Carlo Methods for Nonparametric Models of Sequential Data
Michael C. Hughes, Emily B. Fox and Erik B. Sudderth
Advances in Neural Information Processing Systems 25, 2012


The Nonparametric Metadata Dependent Relational Model
Dae I. Kim, Michael Hughes and Erik B. Sudderth
Proceedings of the 29th International Conference on Machine Learning (ICML-12), 2012


The Doubly Correlated Nonparametric Topic Model
Dae I. Kim and Erik B. Sudderth
Advances in Neural Information Processing Systems 24, 2011


Spatial distance dependent Chinese restaurant processes for image segmentation
Soumya Ghosh, Andrei B. Ungureanu, Erik B. Sudderth and David M. Blei
Advances in Neural Information Processing Systems 24, 2011


Global seismic monitoring as probabilistic inference
Nimar Arora, Stuart Russell, Paul Kidwell and Erik B. Sudderth
Advances in Neural Information Processing Systems 23, 2010


Layered image motion with explicit occlusions, temporal consistency, and depth ordering
Deqing Sun, Erik B. Sudderth and Michael J. Black
Advances in Neural Information Processing Systems 23, 2010


Sharing Features among Dynamical Systems with Beta Processes
Emily B. Fox, Erik B. Sudderth, Michael I. Jordan and Alan S. Willsky
Advances in Neural Information Processing Systems 22, 2009


An HDP-HMM for systems with state persistence
Emily B. Fox, Erik B. Sudderth, Michael I. Jordan and Alan S. Willsky
Proceedings of the 25th International Conference on Machine Learning (ICML-08), 2008


Shared Segmentation of Natural Scenes Using Dependent Pitman-Yor Processes
Erik B. Sudderth and Michael I. Jordan
Advances in Neural Information Processing Systems 21, 2008


Nonparametric Bayesian Learning of Switching Linear Dynamical Systems
Emily B. Fox, Erik B. Sudderth, Alan S. Willsky, Michael I. Jordan and Alan S. Willsky
Advances in Neural Information Processing Systems 21, 2008


Loop Series and Bethe Variational Bounds in Attractive Graphical Models
Alan S. Willsky, Erik B. Sudderth and Martin J. Wainwright
Advances in Neural Information Processing Systems 20, 2007


Describing Visual Scenes using Transformed Dirichlet Processes
Antonio Torralba, Alan S. Willsky, Erik B. Sudderth and William T. Freeman
Advances in Neural Information Processing Systems 18, 2005


Distributed Occlusion Reasoning for Tracking with Nonparametric Belief Propagation
Erik B. Sudderth, Michael I. Mandel, William T. Freeman and Alan S. Willsky
Advances in Neural Information Processing Systems 17, 2004


Efficient Multiscale Sampling from Products of Gaussian Mixtures
Alexander T. Ihler, Erik B. Sudderth, William T. Freeman and Alan S. Willsky
Advances in Neural Information Processing Systems 16, 2003