Erik B. Sudderth

90 papers A* 22A 8C 1Journal 28Unranked 30

Year	Rank	Type	Title / Venue / Authors
2026	A*	`conf`	"It's trained by non-disabled people": Evaluating How Image Quality Affects Product Captioning with Vision-Language Models. CHI Kapil Garg, Xinru Tang, Jimin Heo, Dwayne R. Morgan, Darren Gergle, Erik B. Sudderth, Anne Marie Piper
2025	J	`jnl`	"It's trained by non-disabled people": Evaluating How Image Quality Affects Product Captioning with VLMs. CoRR Kapil Garg, Xinru Tang, Jimin Heo, Dwayne R. Morgan, Darren Gergle, Erik B. Sudderth, Anne Marie Piper
2025	J	`jnl`	Learning to be Smooth: An End-to-End Differentiable Particle Smoother. CoRR Ali Younis, Erik B. Sudderth
2024	J	`jnl`	Bayesian temporal biclustering with applications to multi-subject neuroscience studies. CoRR Federica Zoe Ricci, Erik B. Sudderth, Jaylen Lee, Megan A. K. Peters, Marina Vannucci, Michele Guindani
2024	J	`jnl`	Differentiable and Stable Long-Range Tracking of Multiple Posterior Modes. CoRR Ali Younis, Erik B. Sudderth
2024	A*	`conf`	Learning to be Smooth: An End-to-End Differentiable Particle Smoother. NeurIPS Ali Younis, Erik B. Sudderth
2024	J	`jnl`	VIPaint: Image Inpainting with Pre-Trained Diffusion Models via Variational Inference. CoRR Sakshi Agarwal, Gabe Hoope, Erik B. Sudderth
2023	A	`conf`	A decoder suffices for query-adaptive variational inference. UAI Sakshi Agarwal, Gabriel Hope, Ali Younis, Erik B. Sudderth
2023	A*	`conf`	Differentiable and Stable Long-Range Tracking of Multiple Posterior Modes. NeurIPS Ali Younis, Erik B. Sudderth
2023	J	`jnl`	Unbiased Learning of Deep Generative Models with Structured Discrete Representations. CoRR Harry Bendekgey, Gabriel Hope, Erik B. Sudderth
2023	A*	`conf`	Unbiased learning of deep generative models with structured discrete representations. NeurIPS Henry C. Bendekgey, Gabe Hope, Erik B. Sudderth
2022	A*	`conf`	Thinned random measures for sparse graphs with overlapping communities. NeurIPS Federica Zoe Ricci, Michele Guindani, Erik B. Sudderth
2021	A*	`conf`	Marginalized Stochastic Natural Gradients for Black-Box Variational Inference. ICML Geng Ji, Debora Sujono, Erik B. Sudderth
2021	A*	`conf`	Scalable and Stable Surrogates for Flexible Classifiers with Fairness Constraints. NeurIPS Harry Bendekgey, Erik B. Sudderth
2020	J	`jnl`	Clouds of Oriented Gradients for 3D Detection of Objects, Surfaces, and Indoor Scene Layouts. IEEE Trans. Pattern Anal. Mach. Intell. Zhile Ren, Erik B. Sudderth
2020	J	`jnl`	Learning Consistent Deep Generative Models from Sparse Data via Prediction Constraints. CoRR Gabriel Hope, Madina Abdrakhmanova, Xiaoyin Chen, Michael C. Hughes, Erik B. Sudderth
2019	A*	`conf`	3D Scene Reconstruction With Multi-Layer Depth and Epipolar Transformers. ICCV Daeyun Shin, Zhile Ren, Erik B. Sudderth, Charless C. Fowlkes
2019	A	`conf`	A Fusion Approach for Multi-Frame Optical Flow Estimation. WACV Zhile Ren, Orazio Gallo, Deqing Sun, Ming-Hsuan Yang, Erik B. Sudderth, Jan Kautz
2019	J	`jnl`	Clouds of Oriented Gradients for 3D Detection of Objects, Surfaces, and Indoor Scene Layouts. CoRR Zhile Ren, Erik B. Sudderth
2019	—	`conf`	Multi-layer Depth and Epipolar Feature Transformers for 3D Scene Reconstruction. CVPR Workshops Daeyun Shin, Zhile Ren, Erik B. Sudderth, Charless C. Fowlkes
2019	J	`jnl`	Multi-layer Depth and Epipolar Feature Transformers for 3D Scene Reconstruction. CoRR Daeyun Shin, Zhile Ren, Erik B. Sudderth, Charless C. Fowlkes
2019	A	`conf`	Variational Training for Large-Scale Noisy-OR Bayesian Networks. UAI Geng Ji, Dehua Cheng, Huazhong Ning, Changhe Yuan, Hanning Zhou, Liang Xiong, Erik B. Sudderth
2018	A*	`conf`	3D Object Detection With Latent Support Surfaces. CVPR Zhile Ren, Erik B. Sudderth
2018	J	`jnl`	A Fusion Approach for Multi-Frame Optical Flow Estimation. CoRR Zhile Ren, Orazio Gallo, Deqing Sun, Ming-Hsuan Yang, Erik B. Sudderth, Jan Kautz
2018	—	`conf`	A Simple and Effective Fusion Approach for Multi-frame Optical Flow Estimation. ECCV Workshops (6) Zhile Ren, Orazio Gallo, Deqing Sun, Ming-Hsuan Yang, Erik B. Sudderth, Jan Kautz
2018	A	`conf`	Semi-Supervised Prediction-Constrained Topic Models. AISTATS Michael C. Hughes, Gabriel Hope, Leah Weiner, Thomas H. McCoy Jr., Roy H. Perlis, Erik B. Sudderth, Finale Doshi-Velez
2017	J	`jnl`	Bayesian Paragraph Vectors. CoRR Geng Ji, Robert Bamler, Erik B. Sudderth, Stephan Mandt
2017	—	`conf`	Cascaded Scene Flow Prediction Using Semantic Segmentation. 3DV Zhile Ren, Deqing Sun, Jan Kautz, Erik B. Sudderth
2017	J	`jnl`	Cascaded Scene Flow Prediction using Semantic Segmentation. CoRR Zhile Ren, Deqing Sun, Jan Kautz, Erik B. Sudderth
2017	A*	`conf`	From Patches to Images: A Nonparametric Generative Model. ICML Geng Ji, Michael C. Hughes, Erik B. Sudderth
2017	—	`conf`	Multiscale Semi-Markov Dynamics for Intracortical Brain-Computer Interfaces. NIPS Daniel Milstein, Jason Pacheco, Leigh J. Hochberg, John D. Simeral, Beata Jarosiewicz, Erik B. Sudderth
2017	J	`jnl`	Prediction-Constrained Topic Models for Antidepressant Recommendation. CoRR Michael C. Hughes, Gabriel Hope, Leah Weiner, Thomas H. McCoy Jr., Roy H. Perlis, Erik B. Sudderth, Finale Doshi-Velez
2017	J	`jnl`	Prediction-Constrained Training for Semi-Supervised Mixture and Topic Models. CoRR Michael C. Hughes, Leah Weiner, Gabriel Hope, Thomas H. McCoy Jr., Roy H. Perlis, Erik B. Sudderth, Finale Doshi-Velez
2017	J	`jnl`	Refinery: An Open Source Topic Modeling Web Platform. J. Mach. Learn. Res. Dae Il Kim, Benjamin F. Swanson, Michael C. Hughes, Erik B. Sudderth
2016	J	`jnl`	Fast Learning of Clusters and Topics via Sparse Posteriors. CoRR Michael C. Hughes, Erik B. Sudderth
2016	A*	`conf`	Three-Dimensional Object Detection and Layout Prediction Using Clouds of Oriented Gradients. CVPR Zhile Ren, Erik B. Sudderth
2015	J	`jnl`	Guest Editors' Introduction to the Special Issue on Bayesian Nonparametrics. IEEE Trans. Pattern Anal. Mach. Intell. Ryan P. Adams, Emily B. Fox, Erik B. Sudderth, Yee Whye Teh
2015	A*	`conf`	Layered RGBD scene flow estimation. CVPR Deqing Sun, Erik B. Sudderth, Hanspeter Pfister
2015	A*	`conf`	Proteins, Particles, and Pseudo-Max-Marginals: A Submodular Approach. ICML Jason Pacheco, Erik B. Sudderth
2015	A	`conf`	Reliable and Scalable Variational Inference for the Hierarchical Dirichlet Process. AISTATS Michael C. Hughes, Dae Il Kim, Erik B. Sudderth
2015	—	`conf`	Scalable Adaptation of State Complexity for Nonparametric Hidden Markov Models. NIPS Michael C. Hughes, William T. Stephenson, Erik B. Sudderth
2014	A	`conf`	Nonparametric Clustering with Distance Dependent Hierarchies. UAI Soumya Ghosh, Michalis Raptis, Leonid Sigal, Erik B. Sudderth
2014	A*	`conf`	Preserving Modes and Messages via Diverse Particle Selection. ICML Jason Pacheco, Silvia Zuffi, Michael J. Black, Erik B. Sudderth
2013	A*	`conf`	A Fully-Connected Layered Model of Foreground and Background Flow. CVPR Deqing Sun, Jonas Wulff, Erik B. Sudderth, Hanspeter Pfister, Michael J. Black
2013	—	`conf`	Efficient Online Inference for Bayesian Nonparametric Relational Models. NIPS Dae Il Kim, Prem Gopalan, David M. Blei, Erik B. Sudderth
2013	—	`conf`	Memoized Online Variational Inference for Dirichlet Process Mixture Models. NIPS Michael C. Hughes, Erik B. Sudderth
2012	—	`conf`	Effective Split-Merge Monte Carlo Methods for Nonparametric Models of Sequential Data. NIPS Michael C. Hughes, Emily B. Fox, Erik B. Sudderth
2012	—	`conf`	From Deformations to Parts: Motion-based Segmentation of 3D Objects. NIPS Soumya Ghosh, Erik B. Sudderth, Matthew Loper, Michael J. Black
2012	J	`jnl`	Gibbs Sampling in Open-Universe Stochastic Languages CoRR Nimar S. Arora, Rodrigo de Salvo Braz, Erik B. Sudderth, Stuart Russell
2012	—	`conf`	Improved variational inference for tracking in clutter. SSP Jason L. Pacheco, Erik B. Sudderth
2012	A*	`conf`	Layered segmentation and optical flow estimation over time. CVPR Deqing Sun, Erik B. Sudderth, Michael J. Black
2012	—	`conf`	Minimization of Continuous Bethe Approximations: A Positive Variation. NIPS Jason L. Pacheco, Erik B. Sudderth
2012	—	`conf`	Nonparametric discovery of activity patterns from video collections. CVPR Workshops Michael C. Hughes, Erik B. Sudderth
2012	A*	`conf`	Nonparametric learning for layered segmentation of natural images. CVPR Soumya Ghosh, Erik B. Sudderth
2012	A*	`conf`	The Nonparametric Metadata Dependent Relational Model. ICML Dae Il Kim, Michael C. Hughes, Erik B. Sudderth
2012	—	`conf`	Truly Nonparametric Online Variational Inference for Hierarchical Dirichlet Processes. NIPS Michael Bryant, Erik B. Sudderth
2011	J	`jnl`	Bayesian Nonparametric Inference of Switching Dynamic Linear Models. IEEE Trans. Signal Process. Emily B. Fox, Erik B. Sudderth, Michael I. Jordan, Alan S. Willsky
2011	A*	`conf`	Global Seismic Monitoring: A Bayesian Approach. AAAI Nimar S. Arora, Stuart Russell, Paul Kidwell, Erik B. Sudderth
2011	—	`conf`	Spatial distance dependent Chinese restaurant processes for image segmentation. NIPS Soumya Ghosh, Andrei B. Ungureanu, Erik B. Sudderth, David M. Blei
2011	—	`conf`	The Doubly Correlated Nonparametric Topic Model. NIPS Dae Il Kim, Erik B. Sudderth
2010	—	`conf`	Automatic Inference in BLOG. StarAI@AAAI Nimar S. Arora, Stuart Russell, Erik B. Sudderth
2010	J	`jnl`	Bayesian Nonparametric Methods for Learning Markov Switching Processes. IEEE Signal Process. Mag. Emily B. Fox, Erik B. Sudderth, Michael I. Jordan, Alan S. Willsky
2010	A	`conf`	Gibbs Sampling in Open-Universe Stochastic Languages. UAI Nimar S. Arora, Rodrigo de Salvo Braz, Erik B. Sudderth, Stuart Russell
2010	—	`conf`	Global seismic monitoring as probabilistic inference. NIPS Nimar S. Arora, Stuart Russell, Paul Kidwell, Erik B. Sudderth
2010	—	`conf`	Layered image motion with explicit occlusions, temporal consistency, and depth ordering. NIPS Deqing Sun, Erik B. Sudderth, Michael J. Black
2010	J	`jnl`	Nonparametric belief propagation. Commun. ACM Erik B. Sudderth, Alexander T. Ihler, Michael Isard, William T. Freeman, Alan S. Willsky
2009	J	`jnl`	Guest Editors' Introduction to the Special Section on Probabilistic Graphical Models. IEEE Trans. Pattern Anal. Mach. Intell. Qiang Ji, Jiebo Luo, Dimitris N. Metaxas, Antonio Torralba, Thomas S. Huang, Erik B. Sudderth
2009	A	`conf`	Nonparametric belief propagation for distributed tracking of robot networks with noisy inter-distance measurements. IROS Jeremy Schiff, Erik B. Sudderth, Kenneth Y. Goldberg
2009	—	`conf`	Sharing Features among Dynamical Systems with Beta Processes. NIPS Emily B. Fox, Erik B. Sudderth, Michael I. Jordan, Alan S. Willsky
2008	J	`jnl`	AI's 10 to Watch. IEEE Intell. Syst. 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
2008	A*	`conf`	An HDP-HMM for systems with state persistence. ICML Emily B. Fox, Erik B. Sudderth, Michael I. Jordan, Alan S. Willsky
2008	J	`jnl`	Describing Visual Scenes Using Transformed Objects and Parts. Int. J. Comput. Vis. Erik B. Sudderth, Antonio Torralba, William T. Freeman, Alan S. Willsky
2008	—	`conf`	Nonparametric Bayesian Learning of Switching Linear Dynamical Systems. NIPS Emily B. Fox, Erik B. Sudderth, Michael I. Jordan, Alan S. Willsky
2008	—	`conf`	Shared Segmentation of Natural Scenes Using Dependent Pitman-Yor Processes. NIPS Erik B. Sudderth, Michael I. Jordan
2008	J	`jnl`	Signal and Image Processing with Belief Propagation [DSP Applications]. IEEE Signal Process. Mag. Erik B. Sudderth, William T. Freeman
2007	C	`conf`	Hierarchical Dirichlet processes for tracking maneuvering targets. FUSION Emily B. Fox, Erik B. Sudderth, Alan S. Willsky
2007	—	`conf`	Image Denoising with Nonparametric Hidden Markov Trees. ICIP (3) Jyri J. Kivinen, Erik B. Sudderth, Michael I. Jordan
2007	A*	`conf`	Learning Multiscale Representations of Natural Scenes Using Dirichlet Processes. ICCV Jyri J. Kivinen, Erik B. Sudderth, Michael I. Jordan
2007	—	`conf`	Loop Series and Bethe Variational Bounds in Attractive Graphical Models. NIPS Erik B. Sudderth, Martin J. Wainwright, Alan S. Willsky
2006	—	`conf`	Depth from Familiar Objects: A Hierarchical Model for 3D Scenes. CVPR (2) Erik B. Sudderth, Antonio Torralba, William T. Freeman, Alan S. Willsky
2006	—	`—`	Graphical models for visual object recognition and tracking. Erik B. Sudderth
2005	—	`conf`	Describing Visual Scenes using Transformed Dirichlet Processes. NIPS Erik B. Sudderth, Antonio Torralba, William T. Freeman, Alan S. Willsky
2005	A*	`conf`	Learning Hierarchical Models of Scenes, Objects, and Parts. ICCV Erik B. Sudderth, Antonio Torralba, William T. Freeman, Alan S. Willsky
2004	—	`conf`	Distributed Occlusion Reasoning for Tracking with Nonparametric Belief Propagation. NIPS Erik B. Sudderth, Michael I. Mandel, William T. Freeman, Alan S. Willsky
2004	J	`jnl`	Embedded trees: estimation of Gaussian Processes on graphs with cycles. IEEE Trans. Signal Process. Erik B. Sudderth, Martin J. Wainwright, Alan S. Willsky
2004	—	`conf`	Visual Hand Tracking Using Nonparametric Belief Propagation. CVPR Workshops Erik B. Sudderth, Michael I. Mandel, William T. Freeman, Alan S. Willsky
2003	—	`conf`	Efficient Multiscale Sampling from Products of Gaussian Mixtures. NIPS Alexander T. Ihler, Erik B. Sudderth, William T. Freeman, Alan S. Willsky
2003	—	`conf`	Nonparametric Belief Propagation. CVPR (1) Erik B. Sudderth, Alexander T. Ihler, William T. Freeman, Alan S. Willsky
2002	J	`jnl`	Statistical and Information-Theoretic Methods for Self-Organization and Fusion of Multimodal, Networked Sensors. Int. J. High Perform. Comput. Appl. John W. Fisher III, Martin J. Wainwright, Erik B. Sudderth, Alan S. Willsky
2000	—	`conf`	Tree-Based Modeling and Estimation of Gaussian Processes on Graphs with Cycles. NIPS Martin J. Wainwright, Erik B. Sudderth, Alan S. Willsky