David P. Wipf

According to our database, David P. Wipf authored at least 131 papers between 2003 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2025

Efficient Link Prediction via GNN Layers Induced by Negative Sampling.

Yuxin Wang, Xiannian Hu, Quan Gan, Xuanjing Huang, Xipeng Qiu, David Wipf

IEEE Trans. Knowl. Data Eng., January, 2025

2024

FreshGNN: Reducing Memory Access via Stable Historical Embeddings for Graph Neural Network Training.

Kezhao Huang, Haitian Jiang, Minjie Wang, Guangxuan Xiao, David Wipf, Xiang Song, Quan Gan, Zengfeng Huang, Jidong Zhai, Zheng Zhang

Proc. VLDB Endow., February, 2024

Graph Neural Networks Formed via Layer-wise Ensembles of Heterogeneous Base Models.

Jiuhai Chen, Jonas Mueller, Vassilis N. Ioannidis, Tom Goldstein, David Wipf

Trans. Mach. Learn. Res., 2024

Neural Message Passing Induced by Energy-Constrained Diffusion.

Qitian Wu, David Wipf, Junchi Yan

CoRR, 2024

SGFormer: Single-Layer Graph Transformers with Approximation-Free Linear Complexity.

Qitian Wu, Kai Yang, Hengrui Zhang, David Wipf, Junchi Yan

CoRR, 2024

New Desiderata for Direct Preference Optimization.

Xiangkun Hu, Tong He, David Wipf

CoRR, 2024

4DBInfer: A 4D Benchmarking Toolbox for Graph-Centric Predictive Modeling on Relational DBs.

Minjie Wang, Quan Gan, David Wipf, Zhenkun Cai, Ning Li, Jianheng Tang, Yanlin Zhang, Zizhao Zhang, Zunyao Mao, Yakun Song, Yanbo Wang, Jiahang Li, Han Zhang, Guang Yang, Xiao Qin, Chuan Lei, Muhan Zhang, Weinan Zhang, Christos Faloutsos, Zheng Zhang

CoRR, 2024

BloomGML: Graph Machine Learning through the Lens of Bilevel Optimization.

Amber Yijia Zheng, Tong He, Yixuan Qiu, Minjie Wang, David Wipf

CoRR, 2024

4DBInfer: A 4D Benchmarking Toolbox for Graph-Centric Predictive Modeling on RDBs.

Minjie Wang, Quan Gan, David Wipf, Zhenkun Cai, Ning Li, Jianheng Tang, Yanlin Zhang, Zizhao Zhang, Zunyao Mao, Yakun Song, Yanbo Wang, Jiahang Li, Han Zhang, Guang Yang, Xiao Qin, Chuan Lei, Muhan Zhang, Weinan Zhang, Christos Faloutsos, Zheng Zhang

Proceedings of Workshops at the 50th International Conference on Very Large Data Bases, 2024

GFS: Graph-based Feature Synthesis for Prediction over Relational Databases.

Han Zhang, Quan Gan, David Wipf, Weinan Zhang

Proceedings of Workshops at the 50th International Conference on Very Large Data Bases, 2024

Graph Machine Learning Meets Multi-Table Relational Data.

Quan Gan, Minjie Wang, David Wipf, Christos Faloutsos

Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2024

How Graph Neural Networks Learn: Lessons from Training Dynamics.

Chenxiao Yang, Qitian Wu, David Wipf, Ruoyu Sun, Junchi Yan

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Robust Angular Synchronization via Directed Graph Neural Networks.

Yixuan He, Gesine Reinert, David Wipf, Mihai Cucuringu

Proceedings of the Twelfth International Conference on Learning Representations, 2024

ELF-Gym: Evaluating Large Language Models Generated Features for Tabular Prediction.

Yanlin Zhang, Ning Li, Quan Gan, Weinan Zhang, David Wipf, Minjie Wang

Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, 2024

Graph Machine Learning through the Lens of Bilevel Optimization.

Amber Yijia Zheng, Tong He, Yixuan Qiu, Minjie Wang, David Wipf

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023

Sparse Bayesian Learning for End-to-End EEG Decoding.

Wenlong Wang, Feifei Qi, David Paul Wipf, Chang Cai, Tianyou Yu, Yuanqing Li, Yu Zhang, Zhuliang Yu, Wei Wu

IEEE Trans. Pattern Anal. Mach. Intell., December, 2023

MuseGNN: Interpretable and Convergent Graph Neural Network Layers at Scale.

Haitian Jiang, Renjie Liu, Xiao Yan, Zhenkun Cai, Minjie Wang, David Wipf

CoRR, 2023

How Graph Neural Networks Learn: Lessons from Training Dynamics in Function Space.

Chenxiao Yang, Qitian Wu, David Wipf, Ruoyu Sun, Junchi Yan

CoRR, 2023

ReFresh: Reducing Memory Access from Exploiting Stable Historical Embeddings for Graph Neural Network Training.

Kezhao Huang, Haitian Jiang, Minjie Wang, Guangxuan Xiao, David Wipf, Xiang Song, Quan Gan, Zengfeng Huang, Jidong Zhai, Zheng Zhang

CoRR, 2023

Learning Graph Variational Autoencoders with Constraints and Structured Priors for Conditional Indoor 3D Scene Generation.

Aditya Chattopadhyay, Xi Zhang, David Paul Wipf, Himanshu Arora, René Vidal

Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2023

Marginalization is not Marginal: No Bad VAE Local Minima when Learning Optimal Sparse Representations.

David Wipf

Proceedings of the International Conference on Machine Learning, 2023

From Hypergraph Energy Functions to Hypergraph Neural Networks.

Yuxin Wang, Quan Gan, Xipeng Qiu, Xuanjing Huang, David Wipf

Proceedings of the International Conference on Machine Learning, 2023

On the Initialization of Graph Neural Networks.

Jiahang Li, Yakun Song, Xiang Song, David Wipf

Proceedings of the International Conference on Machine Learning, 2023

DIFFormer: Scalable (Graph) Transformers Induced by Energy Constrained Diffusion.

Qitian Wu, Chenxiao Yang, Wentao Zhao, Yixuan He, David Wipf, Junchi Yan

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Face Restoration via Plug-and-Play 3D Facial Priors.

Xiaobin Hu, Wenqi Ren, Jiaolong Yang, Xiaochun Cao, David Wipf, Bjoern H. Menze, Xin Tong, Hongbin Zha

IEEE Trans. Pattern Anal. Mach. Intell., 2022

A Robust Stacking Framework for Training Deep Graph Models with Multifaceted Node Features.

Jiuhai Chen, Jonas Mueller, Vassilis N. Ioannidis, Tom Goldstein, David Wipf

CoRR, 2022

Learning Enhanced Representations for Tabular Data via Neighborhood Propagation.

Kounianhua Du, Weinan Zhang, Ruiwen Zhou, Yangkun Wang, Xilong Zhao, Jiarui Jin, Quan Gan, Zheng Zhang, David Wipf

CoRR, 2022

Structured Graph Variational Autoencoders for Indoor Furniture layout Generation.

Aditya Chattopadhyay, Xi Zhang, David Paul Wipf, Himanshu Arora, René Vidal

CoRR, 2022

Towards Distribution Shift of Node-Level Prediction on Graphs: An Invariance Perspective.

Qitian Wu, Hengrui Zhang, Junchi Yan, David Wipf

CoRR, 2022

Learning Manifold Dimensions with Conditional Variational Autoencoders.

Yijia Zheng, Tong He, Yixuan Qiu, David P. Wipf

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Self-supervised Amodal Video Object Segmentation.

Jian Yao, Yuxin Hong, Chiyu Wang, Tianjun Xiao, Tong He, Francesco Locatello, David P. Wipf, Yanwei Fu, Zheng Zhang

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Transformers from an Optimization Perspective.

Yongyi Yang, Zengfeng Huang, David P. Wipf

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

NodeFormer: A Scalable Graph Structure Learning Transformer for Node Classification.

Qitian Wu, Wentao Zhao, Zenan Li, David P. Wipf, Junchi Yan

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Learning Enhanced Representation for Tabular Data via Neighborhood Propagation.

Kounianhua Du, Weinan Zhang, Ruiwen Zhou, Yangkun Wang, Xilong Zhao, Jiarui Jin, Quan Gan, Zheng Zhang, David P. Wipf

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Descent Steps of a Relation-Aware Energy Produce Heterogeneous Graph Neural Networks.

Hongjoon Ahn, Yongyi Yang, Quan Gan, Taesup Moon, David P. Wipf

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

GNNRank: Learning Global Rankings from Pairwise Comparisons via Directed Graph Neural Networks.

Yixuan He, Quan Gan, David Wipf, Gesine D. Reinert, Junchi Yan, Mihai Cucuringu

Proceedings of the International Conference on Machine Learning, 2022

Handling Distribution Shifts on Graphs: An Invariance Perspective.

Qitian Wu, Hengrui Zhang, Junchi Yan, David Wipf

Proceedings of the Tenth International Conference on Learning Representations, 2022

Why Propagate Alone? Parallel Use of Labels and Features on Graphs.

Yangkun Wang, Jiarui Jin, Weinan Zhang, Yongyi Yang, Jiuhai Chen, Quan Gan, Yong Yu, Zheng Zhang, Zengfeng Huang, David Wipf

Proceedings of the Tenth International Conference on Learning Representations, 2022

Inductive Relation Prediction Using Analogy Subgraph Embeddings.

Jiarui Jin, Yangkun Wang, Kounianhua Du, Weinan Zhang, Zheng Zhang, David Wipf, Yong Yu, Quan Gan

Proceedings of the Tenth International Conference on Learning Representations, 2022

Does your graph need a confidence boost? Convergent boosted smoothing on graphs with tabular node features.

Jiuhai Chen, Jonas Mueller, Vassilis N. Ioannidis, Soji Adeshina, Yangkun Wang, Tom Goldstein, David Wipf

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

Network In Graph Neural Network.

Xiang Song, Runjie Ma, Jiahang Li, Muhan Zhang, David Paul Wipf

CoRR, 2021

Implicit vs Unfolded Graph Neural Networks.

Yongyi Yang, Yangkun Wang, Zengfeng Huang, David Wipf

CoRR, 2021

Convergent Boosted Smoothing for Modeling Graph Data with Tabular Node Features.

Jiuhai Chen, Jonas Mueller, Vassilis N. Ioannidis, Soji Adeshina, Yangkun Wang, Tom Goldstein, David Wipf

CoRR, 2021

From Canonical Correlation Analysis to Self-supervised Graph Neural Networks.

Hengrui Zhang, Qitian Wu, Junchi Yan, David Wipf, Philip S. Yu

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

A Biased Graph Neural Network Sampler with Near-Optimal Regret.

Qingru Zhang, David Wipf, Quan Gan, Le Song

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

GRIN: Generative Relation and Intention Network for Multi-agent Trajectory Prediction.

Longyuan Li, Jian Yao, Li K. Wenliang, Tong He, Tianjun Xiao, Junchi Yan, David Wipf, Zheng Zhang

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

On the Value of Infinite Gradients in Variational Autoencoder Models.

Bin Dai, Wenliang Li, David P. Wipf

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Graph Neural Networks Inspired by Classical Iterative Algorithms.

Yongyi Yang, Tang Liu, Yangkun Wang, Jinjing Zhou, Quan Gan, Zhewei Wei, Zheng Zhang, Zengfeng Huang, David Wipf

Proceedings of the 38th International Conference on Machine Learning, 2021

Learning Hierarchical Graph Neural Networks for Image Clustering.

Yifan Xing, Tong He, Tianjun Xiao, Yongxin Wang, Yuanjun Xiong, Wei Xia, David Wipf, Zheng Zhang, Stefano Soatto

Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision, 2021

Deep Learning for Linear Inverse Problems Using the Plug-and-Play Priors Framework.

Wei Chen, David Wipf, Miguel Rodrigues

Proceedings of the IEEE International Conference on Acoustics, 2021

Sparse Multi-Path Corrections in Fringe Projection Profilometry.

Yu Zhang, Daniel L. Lau, David Wipf

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2021

Fork or Fail: Cycle-Consistent Training with Many-to-One Mappings.

Qipeng Guo, Zhijing Jin, Ziyu Wang, Xipeng Qiu, Weinan Zhang, Jun Zhu, Zheng Zhang, David Wipf

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

CycleGT: Unsupervised Graph-to-Text and Text-to-Graph Generation via Cycle Training.

Qipeng Guo, Zhijing Jin, Xipeng Qiu, Weinan Zhang, David Wipf, Zheng Zhang

CoRR, 2020

Further Analysis of Outlier Detection with Deep Generative Models.

Ziyu Wang, Bin Dai, David P. Wipf, Jun Zhu

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

The Usual Suspects? Reassessing Blame for VAE Posterior Collapse.

Bin Dai, Ziyu Wang, David P. Wipf

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

Diagnosing and Enhancing VAE Models.

Bin Dai, David P. Wipf

Proceedings of the 7th International Conference on Learning Representations, 2019

Face Video Deblurring Using 3D Facial Priors.

Wenqi Ren, Jiaolong Yang, Senyou Deng, David P. Wipf, Xiaochun Cao, Xin Tong

Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision, 2019

Single Image Reflection Removal Exploiting Misaligned Training Data and Network Enhancements.

Kaixuan Wei, Jiaolong Yang, Ying Fu, David P. Wipf, Hua Huang

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019

2018

Building Invariances Into Sparse Subspace Clustering.

Bo Xin, Yizhou Wang, Wen Gao, David P. Wipf

IEEE Trans. Signal Process., 2018

Image smoothing via unsupervised learning.

Qingnan Fan, Jiaolong Yang, David P. Wipf, Baoquan Chen, Xin Tong

ACM Trans. Graph., 2018

Recurrent Variational Autoencoders for Learning Nonlinear Generative Models in the Presence of Outliers.

Yu Wang, Bin Dai, Gang Hua, John A. D. Aston, David P. Wipf

IEEE J. Sel. Top. Signal Process., 2018

Connections with Robust PCA and the Role of Emergent Sparsity in Variational Autoencoder Models.

Bin Dai, Yu Wang, John A. D. Aston, Gang Hua, David P. Wipf

J. Mach. Learn. Res., 2018

Compressing Neural Networks using the Variational Information Bottleneck.

Bin Dai, Chen Zhu, David P. Wipf

CoRR, 2018

Compressing Neural Networks using the Variational Information Bottleneck.

Bin Dai, Chen Zhu, Baining Guo, David P. Wipf

Proceedings of the 35th International Conference on Machine Learning, 2018

Revisiting Deep Intrinsic Image Decompositions.

Qingnan Fan, Jiaolong Yang, Gang Hua, Baoquan Chen, David P. Wipf

Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition, 2018

2017

An Efficient Joint Formulation for Bayesian Face Verification.

Dong Chen, Xudong Cao, David P. Wipf, Fang Wen, Jian Sun

IEEE Trans. Pattern Anal. Mach. Intell., 2017

From Bayesian Sparsity to Gated Recurrent Nets.

Hao He, Bo Xin, David P. Wipf

CoRR, 2017

Revisiting Deep Image Smoothing and Intrinsic Image Decomposition.

Qingnan Fan, David P. Wipf, Gang Hua, Baoquan Chen

CoRR, 2017

Veiled Attributes of the Variational Autoencoder.

Bin Dai, Yu Wang, John A. D. Aston, Gang Hua, David P. Wipf

CoRR, 2017

Data-Dependent Sparsity for Subspace Clustering.

Bo Xin, Yizhou Wang, Wen Gao, David P. Wipf

Proceedings of the Thirty-Third Conference on Uncertainty in Artificial Intelligence, 2017

Green Generative Modeling: Recycling Dirty Data using Recurrent Variational Autoencoders.

Yu Wang, Bin Dai, Gang Hua, John A. D. Aston, David P. Wipf

Proceedings of the Thirty-Third Conference on Uncertainty in Artificial Intelligence, 2017

From Bayesian Sparsity to Gated Recurrent Nets.

Hao He, Bo Xin, Satoshi Ikehata, David P. Wipf

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

A Generic Deep Architecture for Single Image Reflection Removal and Image Smoothing.

Qingnan Fan, Jiaolong Yang, Gang Hua, Baoquan Chen, David P. Wipf

Proceedings of the IEEE International Conference on Computer Vision, 2017

2016

Exploring Algorithmic Limits of Matrix Rank Minimization Under Affine Constraints.

Bo Xin, Yizhou Wang, Wen Gao, David P. Wipf

IEEE Trans. Signal Process., 2016

Simultaneous Bayesian Sparse Approximation With Structured Sparse Models.

Wei Chen, David P. Wipf, Yu Wang, Yang Liu, Ian J. Wassell

IEEE Trans. Signal Process., 2016

Sparse-as-possible SVBRDF acquisition.

Zhiming Zhou, Guojun Chen, Yue Dong, David P. Wipf, Yong Yu, John M. Snyder, Xin Tong

ACM Trans. Graph., 2016

Maximal Sparsity with Deep Networks?

Bo Xin, Yizhou Wang, Wen Gao, David P. Wipf

CoRR, 2016

Subspace Clustering with a Twist.

David P. Wipf, Yue Dong, Bo Xin

Proceedings of the Thirty-Second Conference on Uncertainty in Artificial Intelligence, 2016

Maximal Sparsity with Deep Networks?

Bo Xin, Yizhou Wang, Wen Gao, David P. Wipf, Baoyuan Wang

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

A Pseudo-Bayesian Algorithm for Robust PCA.

Tae-Hyun Oh, Yasuyuki Matsushita, In-So Kweon, David P. Wipf

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Improving Survey Aggregation with Sparsely Represented Signals.

Tianlin Shi, Forest Agostinelli, Matthew Staib, David P. Wipf, Thomas Moscibroda

Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016

Analysis of Variational Bayesian Factorizations for Sparse and Low-Rank Estimation.

David P. Wipf

Proceedings of the 33nd International Conference on Machine Learning, 2016

2015

New Design Criteria for Robust PCA and a Compliant Bayesian-Inspired Algorithm.

Tae Hyun Oh, David P. Wipf, Yasuyuki Matsushita, In-So Kweon

CoRR, 2015

Clustered Sparse Bayesian Learning.

Yu Wang, David P. Wipf, Jeong-Min Yun, Wei Chen, Ian J. Wassell

Proceedings of the Thirty-First Conference on Uncertainty in Artificial Intelligence, 2015

Pushing the Limits of Affine Rank Minimization by Adapting Probabilistic PCA.

Bo Xin, David P. Wipf

Proceedings of the 32nd International Conference on Machine Learning, 2015

Multi-Task Learning for Subspace Segmentation.

Yu Wang, David P. Wipf, Qing Ling, Wei Chen, Ian J. Wassell

Proceedings of the 32nd International Conference on Machine Learning, 2015

Unsupervised Extraction of Video Highlights via Robust Recurrent Auto-Encoders.

Huan Yang, Baoyuan Wang, Stephen Lin, David P. Wipf, Minyi Guo, Baining Guo

Proceedings of the 2015 IEEE International Conference on Computer Vision, 2015

Augmented Bayesian Compressive Sensing.

David P. Wipf, Jeong-Min Yun, Qing Ling

Proceedings of the 2015 Data Compression Conference, 2015

Understanding and Evaluating Sparse Linear Discriminant Analysis.

Yi Wu, David P. Wipf, Jeong-Min Yun

Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, 2015

2014

Multi-Observation Blind Deconvolution with an Adaptive Sparse Prior.

Haichao Zhang, David P. Wipf, Yanning Zhang

IEEE Trans. Pattern Anal. Mach. Intell., 2014

Photometric Stereo Using Sparse Bayesian Regression for General Diffuse Surfaces.

Satoshi Ikehata, David P. Wipf, Yasuyuki Matsushita, Kiyoharu Aizawa

IEEE Trans. Pattern Anal. Mach. Intell., 2014

Revisiting Bayesian blind deconvolution.

David P. Wipf, Haichao Zhang

J. Mach. Learn. Res., 2014

Exploring Algorithmic Limits of Matrix Rank Minimization under Affine Constraints.

Bo Xin, David P. Wipf

CoRR, 2014

Exploiting the convex-concave penalty for tracking: A novel dynamic reweighted sparse Bayesian learning algorithm.

Yu Wang, David P. Wipf, Wei Chen, Ian J. Wassell

Proceedings of the IEEE International Conference on Acoustics, 2014

2013

Non-Uniform Blind Deblurring with a Spatially-Adaptive Sparse Prior.

Haichao Zhang, David P. Wipf

CoRR, 2013

Non-Uniform Camera Shake Removal Using a Spatially-Adaptive Sparse Penalty.

Haichao Zhang, David P. Wipf

Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

A hierarchical Bayesian M/EEG imagingmethod correcting for incomplete spatio-temporal priors.

Carsten Stahlhut, Hagai Thomas Attias, Kensuke Sekihara, David P. Wipf, Lars Kai Hansen, Srikantan S. Nagarajan

Proceedings of the 10th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2013

Fixed-Point Model For Structured Labeling.

Quannan Li, Jingdong Wang, David P. Wipf, Zhuowen Tu

Proceedings of the 30th International Conference on Machine Learning, 2013

A Practical Transfer Learning Algorithm for Face Verification.

Xudong Cao, David P. Wipf, Fang Wen, Genquan Duan, Jian Sun

Proceedings of the IEEE International Conference on Computer Vision, 2013

Analysis of Bayesian Blind Deconvolution.

David P. Wipf, Haichao Zhang

Proceedings of the Energy Minimization Methods in Computer Vision and Pattern Recognition, 2013

Multi-image Blind Deblurring Using a Coupled Adaptive Sparse Prior.

Haichao Zhang, David P. Wipf, Yanning Zhang

Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition, 2013

2012

Performance evaluation of the Champagne source reconstruction algorithm on simulated and real M/EEG data.

Julia P. Owen, David P. Wipf, Hagai Thomas Attias, Kensuke Sekihara, Srikantan S. Nagarajan

NeuroImage, 2012

Image Super-Resolution via Sparse Bayesian Modeling of Natural Images

Haichao Zhang, David P. Wipf, Yanning Zhang

CoRR, 2012

Non-Convex Rank Minimization via an Empirical Bayesian Approach.

David P. Wipf

Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence, 2012

Dual-Space Analysis of the Sparse Linear Model.

David P. Wipf, Yi Wu

Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

Learning sparse covariance patterns for natural scenes.

Liwei Wang, Yin Li, Jiaya Jia, Jian Sun, David P. Wipf, James M. Rehg

Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition, 2012

Robust photometric stereo using sparse regression.

Satoshi Ikehata, David P. Wipf, Yasuyuki Matsushita, Kiyoharu Aizawa

Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition, 2012

2011

Latent Variable Bayesian Models for Promoting Sparsity.

David P. Wipf, Bhaskar D. Rao, Srikantan S. Nagarajan

IEEE Trans. Inf. Theory, 2011

Sparse Estimation with Structured Dictionaries.

David P. Wipf

Proceedings of the Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, 2011

2010

Variational Bayesian Inference Techniques.

Matthias W. Seeger, David P. Wipf

IEEE Signal Process. Mag., 2010

Robust Bayesian estimation of the location, orientation, and time course of multiple correlated neural sources using MEG.

David P. Wipf, Julia P. Owen, Hagai Thomas Attias, Kensuke Sekihara, Srikantan S. Nagarajan

NeuroImage, 2010

Iterative Reweighted ₁ and ₂ Methods for Finding Sparse Solutions.

David P. Wipf, Srikantan S. Nagarajan

IEEE J. Sel. Top. Signal Process., 2010

Sparse Spatio-temporal Inference of Electromagnetic Brain Sources.

Carsten Stahlhut, Hagai Attias, David P. Wipf, Lars Kai Hansen, Srikantan S. Nagarajan

Proceedings of the Machine Learning in Medical Imaging, First International Workshop, 2010

2009

A unified Bayesian framework for MEG/EEG source imaging.

David P. Wipf, Srikantan S. Nagarajan

NeuroImage, 2009

Sparse Estimation Using General Likelihoods and Non-Factorial Priors.

David P. Wipf, Srikantan S. Nagarajan

Proceedings of the Advances in Neural Information Processing Systems 22: 23rd Annual Conference on Neural Information Processing Systems 2009. Proceedings of a meeting held 7-10 December 2009, 2009

Robust Methods for Reconstructing Brain Activity and Functional Connectivity Between Brain Sources with MEG/EEG Data.

Julia P. Owen, David P. Wipf, Hagai Attias, Kensuke Sekihara, Srikantan S. Nagarajan

Proceedings of the 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Boston, MA, USA, June 28, 2009

2008

Estimating the Location and Orientation of Complex, Correlated Neural Activity using MEG.

David P. Wipf, Julia P. Owen, Hagai Attias, Kensuke Sekihara, Srikantan S. Nagarajan

Proceedings of the Advances in Neural Information Processing Systems 21, 2008

2007

An Empirical Bayesian Strategy for Solving the Simultaneous Sparse Approximation Problem.

David P. Wipf, Bhaskar D. Rao

IEEE Trans. Signal Process., 2007

Lane Change Intent Analysis Using Robust Operators and Sparse Bayesian Learning.

Joel C. McCall, David P. Wipf, Mohan M. Trivedi, Bhaskar D. Rao

IEEE Trans. Intell. Transp. Syst., 2007

A New View of Automatic Relevance Determination.

David P. Wipf, Srikantan S. Nagarajan

Proceedings of the Advances in Neural Information Processing Systems 20, 2007

Beamforming using the relevance vector machine.

David P. Wipf, Srikantan S. Nagarajan

Proceedings of the Machine Learning, 2007

Performance Evaluation of Latent Variable Models with Sparse Priors.

David P. Wipf, Jason A. Palmer, Bhaskar D. Rao, Kenneth Kreutz-Delgado

Proceedings of the IEEE International Conference on Acoustics, 2007

2006

Bayesian methods for finding sparse representations.

David P. Wipf

PhD thesis, 2006

Analysis of Empirical Bayesian Methods for Neuroelectromagnetic Source Localization.

David P. Wipf, Rey Ramírez, Jason A. Palmer, Scott Makeig, Bhaskar D. Rao

Proceedings of the Advances in Neural Information Processing Systems 19, 2006

2005

Comparing the Effects of Different Weight Distributions on Finding Sparse Representations.

David P. Wipf, Bhaskar D. Rao

Proceedings of the Advances in Neural Information Processing Systems 18 [Neural Information Processing Systems, 2005

Variational EM Algorithms for Non-Gaussian Latent Variable Models.

Jason A. Palmer, David P. Wipf, Kenneth Kreutz-Delgado, Bhaskar D. Rao

Proceedings of the Advances in Neural Information Processing Systems 18 [Neural Information Processing Systems, 2005

2004

Sparse Bayesian learning for basis selection.

David P. Wipf, Bhaskar D. Rao

IEEE Trans. Signal Process., 2004

L_0-norm Minimization for Basis Selection.

David P. Wipf, Bhaskar D. Rao

Proceedings of the Advances in Neural Information Processing Systems 17 [Neural Information Processing Systems, 2004

Probabilistic analysis for basis selection via ℓ_p diversity measures.

David P. Wipf, Bhaskar D. Rao

Proceedings of the 2004 IEEE International Conference on Acoustics, 2004

2003

Perspectives on Sparse Bayesian Learning.

David P. Wipf, Jason A. Palmer, Bhaskar D. Rao

Proceedings of the Advances in Neural Information Processing Systems 16 [Neural Information Processing Systems, 2003

Bayesian learning for sparse signal reconstruction.

David P. Wipf, Bhaskar D. Rao

Proceedings of the 2003 IEEE International Conference on Acoustics, 2003