Tree-Based Modeling and Estimation of Gaussian Processes on Graphs with Cycles. - Martin J. Wainwright; Erik B. Sudderth; Alan S. Willsky (NIPS, 2000)

Statistical and Information-Theoretic Methods for Self-Organization and Fusion of Multimodal, Networked Sensors. - John W. Fisher III; Martin J. Wainwright; Erik B. Sudderth; Alan S. Willsky (Int. J. High Perform. Comput. Appl., 2002)

Nonparametric Belief Propagation. - Erik B. Sudderth; Alexander T. Ihler; William T. Freeman; Alan S. Willsky (CVPR (1), 2003)

Efficient Multiscale Sampling from Products of Gaussian Mixtures. - Alexander T. Ihler; Erik B. Sudderth; William T. Freeman; Alan S. Willsky (NIPS, 2003)

Visual Hand Tracking Using Nonparametric Belief Propagation. - Erik B. Sudderth; Michael I. Mandel; William T. Freeman; Alan S. Willsky (CVPR Workshops, 2004)

Distributed Occlusion Reasoning for Tracking with Nonparametric Belief Propagation. - Erik B. Sudderth; Michael I. Mandel; William T. Freeman; Alan S. Willsky (NIPS, 2004)

Embedded trees: estimation of Gaussian Processes on graphs with cycles. - Erik B. Sudderth; Martin J. Wainwright; Alan S. Willsky (IEEE Trans. Signal Process., 2004)

Learning Hierarchical Models of Scenes, Objects, and Parts. - Erik B. Sudderth; Antonio Torralba 001; William T. Freeman; Alan S. Willsky (ICCV, 2005)

Describing Visual Scenes using Transformed Dirichlet Processes. - Erik B. Sudderth; Antonio Torralba 001; William T. Freeman; Alan S. Willsky (NIPS, 2005)

Depth from Familiar Objects: A Hierarchical Model for 3D Scenes. - Erik B. Sudderth; Antonio Torralba 001; William T. Freeman; Alan S. Willsky (CVPR (2), 2006)

Graphical models for visual object recognition and tracking. - Erik B. Sudderth (2006)

Hierarchical Dirichlet processes for tracking maneuvering targets. - Emily B. Fox; Erik B. Sudderth; Alan S. Willsky (FUSION, 2007)

Learning Multiscale Representations of Natural Scenes Using Dirichlet Processes. - Jyri J. Kivinen; Erik B. Sudderth; Michael I. Jordan (ICCV, 2007)

Image Denoising with Nonparametric Hidden Markov Trees. - Jyri J. Kivinen; Erik B. Sudderth; Michael I. Jordan (ICIP (3), 2007)

Loop Series and Bethe Variational Bounds in Attractive Graphical Models. - Erik B. Sudderth; Martin J. Wainwright; Alan S. Willsky (NIPS, 2007)

An HDP-HMM for systems with state persistence. - Emily B. Fox; Erik B. Sudderth; Michael I. Jordan; Alan S. Willsky (ICML, 2008)

Nonparametric Bayesian Learning of Switching Linear Dynamical Systems. - Emily B. Fox; Erik B. Sudderth; Michael I. Jordan; Alan S. Willsky (NIPS, 2008)

Shared Segmentation of Natural Scenes Using Dependent Pitman-Yor Processes. - Erik B. Sudderth; Michael I. Jordan (NIPS, 2008)

AI's 10 to Watch. - James A. Hendler; Philipp Cimiano; Dmitri A. Dolgov; Anat Levin; Peter Mika; Brian Milch; Louis-Philippe Morency; Boris Motik; Jennifer Neville; Erik B. Sudderth; Luis von Ahn (IEEE Intell. Syst., 2008)

Describing Visual Scenes Using Transformed Objects and Parts. - Erik B. Sudderth; Antonio Torralba 001; William T. Freeman; Alan S. Willsky (Int. J. Comput. Vis., 2008)

Signal and Image Processing with Belief Propagation [DSP Applications]. - Erik B. Sudderth; William T. Freeman (IEEE Signal Process. Mag., 2008)

Nonparametric belief propagation for distributed tracking of robot networks with noisy inter-distance measurements. - Jeremy Schiff; Erik B. Sudderth; Kenneth Y. Goldberg (IROS, 2009)

Sharing Features among Dynamical Systems with Beta Processes. - Emily B. Fox; Erik B. Sudderth; Michael I. Jordan; Alan S. Willsky (NIPS, 2009)

Guest Editors' Introduction to the Special Section on Probabilistic Graphical Models. - Qiang Ji; Jiebo Luo 001; Dimitris N. Metaxas; Antonio Torralba 001; Thomas S. Huang; Erik B. Sudderth (IEEE Trans. Pattern Anal. Mach. Intell., 2009)

Automatic Inference in BLOG. - Nimar S. Arora; Stuart Russell 001; Erik B. Sudderth (StarAI@AAAI, 2010)

Global seismic monitoring as probabilistic inference. - Nimar S. Arora; Stuart Russell 001; Paul Kidwell; Erik B. Sudderth (NIPS, 2010)

Layered image motion with explicit occlusions, temporal consistency, and depth ordering. - Deqing Sun; Erik B. Sudderth; Michael J. Black (NIPS, 2010)

Gibbs Sampling in Open-Universe Stochastic Languages. - Nimar S. Arora; Rodrigo de Salvo Braz; Erik B. Sudderth; Stuart Russell 001 (UAI, 2010)

Nonparametric belief propagation. - Erik B. Sudderth; Alexander T. Ihler; Michael Isard; William T. Freeman; Alan S. Willsky (Commun. ACM, 2010)

Bayesian Nonparametric Methods for Learning Markov Switching Processes. - Emily B. Fox; Erik B. Sudderth; Michael I. Jordan; Alan S. Willsky (IEEE Signal Process. Mag., 2010)

Global Seismic Monitoring: A Bayesian Approach. - Nimar S. Arora; Stuart Russell 001; Paul Kidwell; Erik B. Sudderth (AAAI, 2011)

Spatial distance dependent Chinese restaurant processes for image segmentation. - Soumya Ghosh; Andrei B. Ungureanu; Erik B. Sudderth; David M. Blei (NIPS, 2011)

The Doubly Correlated Nonparametric Topic Model. - Dae Il Kim; Erik B. Sudderth (NIPS, 2011)

Bayesian Nonparametric Inference of Switching Dynamic Linear Models. - Emily B. Fox; Erik B. Sudderth; Michael I. Jordan; Alan S. Willsky (IEEE Trans. Signal Process., 2011)

Nonparametric learning for layered segmentation of natural images. - Soumya Ghosh; Erik B. Sudderth (CVPR, 2012)

Nonparametric discovery of activity patterns from video collections. - Michael C. Hughes; Erik B. Sudderth (CVPR Workshops, 2012)

Layered segmentation and optical flow estimation over time. - Deqing Sun; Erik B. Sudderth; Michael J. Black (CVPR, 2012)

The Nonparametric Metadata Dependent Relational Model. - Dae Il Kim; Michael C. Hughes; Erik B. Sudderth (ICML, 2012)

Truly Nonparametric Online Variational Inference for Hierarchical Dirichlet Processes. - Michael Bryant 002; Erik B. Sudderth (NIPS, 2012)

From Deformations to Parts: Motion-based Segmentation of 3D Objects. - Soumya Ghosh; Erik B. Sudderth; Matthew Loper; Michael J. Black (NIPS, 2012)

Effective Split-Merge Monte Carlo Methods for Nonparametric Models of Sequential Data. - Michael C. Hughes; Emily B. Fox; Erik B. Sudderth (NIPS, 2012)

Minimization of Continuous Bethe Approximations: A Positive Variation. - Jason L. Pacheco; Erik B. Sudderth (NIPS, 2012)

Improved variational inference for tracking in clutter. - Jason L. Pacheco; Erik B. Sudderth (SSP, 2012)

A Fully-Connected Layered Model of Foreground and Background Flow. - Deqing Sun; Jonas Wulff; Erik B. Sudderth; Hanspeter Pfister; Michael J. Black (CVPR, 2013)

Memoized Online Variational Inference for Dirichlet Process Mixture Models. - Michael C. Hughes; Erik B. Sudderth (NIPS, 2013)

Efficient Online Inference for Bayesian Nonparametric Relational Models. - Dae Il Kim; Prem Gopalan; David M. Blei; Erik B. Sudderth (NIPS, 2013)

Preserving Modes and Messages via Diverse Particle Selection. - Jason Pacheco; Silvia Zuffi; Michael J. Black; Erik B. Sudderth (ICML, 2014)

Nonparametric Clustering with Distance Dependent Hierarchies. - Soumya Ghosh; Michalis Raptis; Leonid Sigal; Erik B. Sudderth (UAI, 2014)

Reliable and Scalable Variational Inference for the Hierarchical Dirichlet Process. - Michael C. Hughes; Dae Il Kim; Erik B. Sudderth (AISTATS, 2015)

Layered RGBD scene flow estimation. - Deqing Sun; Erik B. Sudderth; Hanspeter Pfister (CVPR, 2015)

Proteins, Particles, and Pseudo-Max-Marginals: A Submodular Approach. - Jason Pacheco; Erik B. Sudderth (ICML, 2015)

Scalable Adaptation of State Complexity for Nonparametric Hidden Markov Models. - Michael C. Hughes; William T. Stephenson; Erik B. Sudderth (NIPS, 2015)

Guest Editors' Introduction to the Special Issue on Bayesian Nonparametrics. - Ryan P. Adams; Emily B. Fox; Erik B. Sudderth; Yee Whye Teh (IEEE Trans. Pattern Anal. Mach. Intell., 2015)

Three-Dimensional Object Detection and Layout Prediction Using Clouds of Oriented Gradients. - Zhile Ren; Erik B. Sudderth (CVPR, 2016)

Fast Learning of Clusters and Topics via Sparse Posteriors. - Michael C. Hughes; Erik B. Sudderth (CoRR, 2016)

Cascaded Scene Flow Prediction Using Semantic Segmentation. - Zhile Ren; Deqing Sun; Jan Kautz; Erik B. Sudderth (3DV, 2017)

From Patches to Images: A Nonparametric Generative Model. - Geng Ji 001; Michael C. Hughes; Erik B. Sudderth (ICML, 2017)

Multiscale Semi-Markov Dynamics for Intracortical Brain-Computer Interfaces. - Daniel Milstein; Jason Pacheco; Leigh J. Hochberg; John D. Simeral; Beata Jarosiewicz; Erik B. Sudderth (NIPS, 2017)

Prediction-Constrained Training for Semi-Supervised Mixture and Topic Models. - Michael C. Hughes; Leah Weiner; Gabriel Hope; Thomas H. McCoy Jr.; Roy H. Perlis; Erik B. Sudderth; Finale Doshi-Velez (CoRR, 2017)

Bayesian Paragraph Vectors. - Geng Ji 001; Robert Bamler; Erik B. Sudderth; Stephan Mandt (CoRR, 2017)

Prediction-Constrained Topic Models for Antidepressant Recommendation. - Michael C. Hughes; Gabriel Hope; Leah Weiner; Thomas H. McCoy Jr.; Roy H. Perlis; Erik B. Sudderth; Finale Doshi-Velez (CoRR, 2017)

Refinery: An Open Source Topic Modeling Web Platform. - Dae Il Kim; Benjamin F. Swanson; Michael C. Hughes; Erik B. Sudderth (J. Mach. Learn. Res., 2017)

Semi-Supervised Prediction-Constrained Topic Models. - Michael C. Hughes; Gabriel Hope; Leah Weiner; Thomas H. McCoy Jr.; Roy H. Perlis; Erik B. Sudderth; Finale Doshi-Velez (AISTATS, 2018)

3D Object Detection With Latent Support Surfaces. - Zhile Ren; Erik B. Sudderth (CVPR, 2018)

A Simple and Effective Fusion Approach for Multi-frame Optical Flow Estimation. - Zhile Ren; Orazio Gallo; Deqing Sun; Ming-Hsuan Yang 001; Erik B. Sudderth; Jan Kautz (ECCV Workshops (6), 2018)

Multi-layer Depth and Epipolar Feature Transformers for 3D Scene Reconstruction. - Daeyun Shin; Zhile Ren; Erik B. Sudderth; Charless C. Fowlkes (CVPR Workshops, 2019)

3D Scene Reconstruction With Multi-Layer Depth and Epipolar Transformers. - Daeyun Shin; Zhile Ren; Erik B. Sudderth; Charless C. Fowlkes (ICCV, 2019)

Variational Training for Large-Scale Noisy-OR Bayesian Networks. - Geng Ji 001; Dehua Cheng; Huazhong Ning; Changhe Yuan; Hanning Zhou; Liang Xiong; Erik B. Sudderth (UAI, 2019)

A Fusion Approach for Multi-Frame Optical Flow Estimation. - Zhile Ren; Orazio Gallo; Deqing Sun; Ming-Hsuan Yang 001; Erik B. Sudderth; Jan Kautz (WACV, 2019)

Learning Consistent Deep Generative Models from Sparse Data via Prediction Constraints. - Gabriel Hope; Madina Abdrakhmanova; Xiaoyin Chen; Michael C. Hughes; Erik B. Sudderth (CoRR, 2020)

Clouds of Oriented Gradients for 3D Detection of Objects, Surfaces, and Indoor Scene Layouts. - Zhile Ren; Erik B. Sudderth (IEEE Trans. Pattern Anal. Mach. Intell., 2020)

Marginalized Stochastic Natural Gradients for Black-Box Variational Inference. - Geng Ji 001; Debora Sujono; Erik B. Sudderth (ICML, 2021)

Scalable and Stable Surrogates for Flexible Classifiers with Fairness Constraints. - Harry Bendekgey; Erik B. Sudderth (NeurIPS, 2021)

Thinned random measures for sparse graphs with overlapping communities. - Federica Zoe Ricci; Michele Guindani; Erik B. Sudderth (NeurIPS, 2022)

Unbiased learning of deep generative models with structured discrete representations. - Henry C. Bendekgey; Gabe Hope; Erik B. Sudderth (NeurIPS, 2023)

Differentiable and Stable Long-Range Tracking of Multiple Posterior Modes. - Ali Younis; Erik B. Sudderth (NeurIPS, 2023)

A decoder suffices for query-adaptive variational inference. - Sakshi Agarwal; Gabriel Hope; Ali Younis; Erik B. Sudderth (UAI, 2023)

Unbiased Learning of Deep Generative Models with Structured Discrete Representations. - Harry Bendekgey; Gabriel Hope; Erik B. Sudderth (CoRR, 2023)

Learning to be Smooth: An End-to-End Differentiable Particle Smoother. - Ali Younis; Erik B. Sudderth (NeurIPS, 2024)

Bayesian temporal biclustering with applications to multi-subject neuroscience studies. - Federica Zoe Ricci; Erik B. Sudderth; Jaylen Lee; Megan A. K. Peters; Marina Vannucci; Michele Guindani (CoRR, 2024)

VIPaint: Image Inpainting with Pre-Trained Diffusion Models via Variational Inference. - Sakshi Agarwal; Gabe Hoope; Erik B. Sudderth (CoRR, 2024)