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ORCIDDavid P. Wipf
David Paul Wipf – David Wipf
Person information
- affiliation: Amazon Web Services Inc., Shanghai, China
- affiliation (2011 - 2020): Microsoft Research Asia, Visual Computing Group
- affiliation (2007 - 2011): University of California, San Francisco, Biomagnetic Imaging Lab
- affiliation (PhD 2007): University of California, San Diego, Digital Signal Processing Lab
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2020 – today
- 2024
[j23]Kezhao Huang, Haitian Jiang, Minjie Wang, Guangxuan Xiao, David Wipf, Xiang Song, Quan Gan, Zengfeng Huang, Jidong Zhai, Zheng Zhang:
FreshGNN: Reducing Memory Access via Stable Historical Embeddings for Graph Neural Network Training. Proc. VLDB Endow. 17(6): 1473-1486 (2024)- [i50]Amber Yijia Zheng, Tong He, Yixuan Qiu, Minjie Wang, David Wipf:
BloomGML: Graph Machine Learning through the Lens of Bilevel Optimization. CoRR abs/2403.04763 (2024) - 2023
- [j22]Wenlong Wang
, Feifei Qi, David Paul Wipf, Chang Cai, Tianyou Yu, Yuanqing Li, Yu Zhang, Zhuliang Yu, Wei Wu:
Sparse Bayesian Learning for End-to-End EEG Decoding. IEEE Trans. Pattern Anal. Mach. Intell. 45(12): 15632-15649 (2023) - [c75]Qitian Wu, Chenxiao Yang, Wentao Zhao, Yixuan He, David Wipf, Junchi Yan:
DIFFormer: Scalable (Graph) Transformers Induced by Energy Constrained Diffusion. ICLR 2023 - [c74]Jiahang Li, Yakun Song, Xiang Song, David Wipf:
On the Initialization of Graph Neural Networks. ICML 2023: 19911-19931 - [c73]Yuxin Wang, Quan Gan, Xipeng Qiu, Xuanjing Huang, David Wipf:
From Hypergraph Energy Functions to Hypergraph Neural Networks. ICML 2023: 35605-35623 - [c72]David Wipf:
Marginalization is not Marginal: No Bad VAE Local Minima when Learning Optimal Sparse Representations. ICML 2023: 37108-37132 - [c71]Aditya Chattopadhyay, Xi Zhang, David Paul Wipf, Himanshu Arora, René Vidal:
Learning Graph Variational Autoencoders with Constraints and Structured Priors for Conditional Indoor 3D Scene Generation. WACV 2023: 785-794 - [i49]Kezhao Huang, Haitian Jiang, Minjie Wang, Guangxuan Xiao, David Wipf, Xiang Song, Quan Gan, Zengfeng Huang, Jidong Zhai, Zheng Zhang:
ReFresh: Reducing Memory Access from Exploiting Stable Historical Embeddings for Graph Neural Network Training. CoRR abs/2301.07482 (2023) - [i48]Qitian Wu, Chenxiao Yang, Wentao Zhao, Yixuan He, David Wipf, Junchi Yan:
DIFFormer: Scalable (Graph) Transformers Induced by Energy Constrained Diffusion. CoRR abs/2301.09474 (2023) - [i47]Yijia Zheng, Tong He, Yixuan Qiu, David Wipf:
Learning Manifold Dimensions with Conditional Variational Autoencoders. CoRR abs/2302.11756 (2023) - [i46]Qitian Wu, Wentao Zhao, Zenan Li, David Wipf, Junchi Yan:
NodeFormer: A Scalable Graph Structure Learning Transformer for Node Classification. CoRR abs/2306.08385 (2023) - [i45]Yuxin Wang, Quan Gan, Xipeng Qiu, Xuanjing Huang, David Wipf:
From Hypergraph Energy Functions to Hypergraph Neural Networks. CoRR abs/2306.09623 (2023) - [i44]Chenxiao Yang, Qitian Wu, David Wipf, Ruoyu Sun, Junchi Yan:
How Graph Neural Networks Learn: Lessons from Training Dynamics in Function Space. CoRR abs/2310.05105 (2023) - [i43]Yixuan He, Gesine Reinert, David Wipf, Mihai Cucuringu:
Robust Angular Synchronization via Directed Graph Neural Networks. CoRR abs/2310.05842 (2023) - [i42]Yuxin Wang, Xiannian Hu, Quan Gan, Xuanjing Huang, Xipeng Qiu, David Wipf:
Efficient Link Prediction via GNN Layers Induced by Negative Sampling. CoRR abs/2310.09516 (2023) - [i41]Haitian Jiang, Renjie Liu, Xiao Yan, Zhenkun Cai, Minjie Wang, David Wipf:
MuseGNN: Interpretable and Convergent Graph Neural Network Layers at Scale. CoRR abs/2310.12457 (2023) - [i40]Han Zhang, Quan Gan, David Wipf, Weinan Zhang:
GFS: Graph-based Feature Synthesis for Prediction over Relational Databases. CoRR abs/2312.02037 (2023) - [i39]Jiahang Li, Yakun Song, Xiang Song, David Paul Wipf:
On the Initialization of Graph Neural Networks. CoRR abs/2312.02622 (2023) - 2022
- [j21]Xiaobin Hu, Wenqi Ren, Jiaolong Yang, Xiaochun Cao, David Wipf, Bjoern H. Menze, Xin Tong, Hongbin Zha:
Face Restoration via Plug-and-Play 3D Facial Priors. IEEE Trans. Pattern Anal. Mach. Intell. 44(12): 8910-8926 (2022) - [c70]Jiuhai Chen, Jonas Mueller, Vassilis N. Ioannidis, Soji Adeshina, Yangkun Wang, Tom Goldstein, David Wipf:
Does your graph need a confidence boost? Convergent boosted smoothing on graphs with tabular node features. ICLR 2022 - [c69]Jiarui Jin, Yangkun Wang, Kounianhua Du, Weinan Zhang, Zheng Zhang, David Wipf, Yong Yu, Quan Gan:
Inductive Relation Prediction Using Analogy Subgraph Embeddings. ICLR 2022 - [c68]Yangkun Wang, Jiarui Jin, Weinan Zhang, Yongyi Yang, Jiuhai Chen, Quan Gan, Yong Yu, Zheng Zhang, Zengfeng Huang, David Wipf:
Why Propagate Alone? Parallel Use of Labels and Features on Graphs. ICLR 2022 - [c67]Qitian Wu, Hengrui Zhang, Junchi Yan, David Wipf:
Handling Distribution Shifts on Graphs: An Invariance Perspective. ICLR 2022 - [c66]Yixuan He, Quan Gan, David Wipf, Gesine D. Reinert, Junchi Yan, Mihai Cucuringu:
GNNRank: Learning Global Rankings from Pairwise Comparisons via Directed Graph Neural Networks. ICML 2022: 8581-8612 - [c65]Hongjoon Ahn, Yongyi Yang, Quan Gan, Taesup Moon, David P. Wipf:
Descent Steps of a Relation-Aware Energy Produce Heterogeneous Graph Neural Networks. NeurIPS 2022 - [c64]Kounianhua Du, Weinan Zhang, Ruiwen Zhou, Yangkun Wang, Xilong Zhao, Jiarui Jin, Quan Gan, Zheng Zhang, David P. Wipf:
Learning Enhanced Representation for Tabular Data via Neighborhood Propagation. NeurIPS 2022 - [c63]Qitian Wu, Wentao Zhao, Zenan Li, David P. Wipf, Junchi Yan:
NodeFormer: A Scalable Graph Structure Learning Transformer for Node Classification. NeurIPS 2022 - [c62]Yongyi Yang, Zengfeng Huang, David P. Wipf:
Transformers from an Optimization Perspective. NeurIPS 2022 - [c61]Jian Yao, Yuxin Hong, Chiyu Wang, Tianjun Xiao, Tong He, Francesco Locatello, David P. Wipf, Yanwei Fu, Zheng Zhang:
Self-supervised Amodal Video Object Segmentation. NeurIPS 2022 - [c60]Yijia Zheng, Tong He, Yixuan Qiu, David P. Wipf:
Learning Manifold Dimensions with Conditional Variational Autoencoders. NeurIPS 2022 - [i38]Yixuan He, Quan Gan, David Wipf, Gesine Reinert, Junchi Yan, Mihai Cucuringu:
GNNRank: Learning Global Rankings from Pairwise Comparisons via Directed Graph Neural Networks. CoRR abs/2202.00211 (2022) - [i37]Qitian Wu, Hengrui Zhang, Junchi Yan, David Wipf:
Towards Distribution Shift of Node-Level Prediction on Graphs: An Invariance Perspective. CoRR abs/2202.02466 (2022) - [i36]Aditya Chattopadhyay, Xi Zhang, David Paul Wipf, Himanshu Arora, René Vidal:
Structured Graph Variational Autoencoders for Indoor Furniture layout Generation. CoRR abs/2204.04867 (2022) - [i35]Yongyi Yang, Zengfeng Huang, David Wipf:
Transformers from an Optimization Perspective. CoRR abs/2205.13891 (2022) - [i34]Kounianhua Du, Weinan Zhang, Ruiwen Zhou, Yangkun Wang, Xilong Zhao, Jiarui Jin, Quan Gan, Zheng Zhang, David Wipf:
Learning Enhanced Representations for Tabular Data via Neighborhood Propagation. CoRR abs/2206.06587 (2022) - [i33]Jiuhai Chen, Jonas Mueller, Vassilis N. Ioannidis, Tom Goldstein, David Wipf:
A Robust Stacking Framework for Training Deep Graph Models with Multifaceted Node Features. CoRR abs/2206.08473 (2022) - [i32]Hongjoon Ahn, Yongyi Yang, Quan Gan, David Wipf, Taesup Moon:
Descent Steps of a Relation-Aware Energy Produce Heterogeneous Graph Neural Networks. CoRR abs/2206.11081 (2022) - [i31]Jian Yao, Yuxin Hong, Chiyu Wang, Tianjun Xiao, Tong He, Francesco Locatello, David Wipf, Yanwei Fu, Zheng Zhang:
Self-supervised Amodal Video Object Segmentation. CoRR abs/2210.12733 (2022) - 2021
- [c59]Qipeng Guo, Zhijing Jin, Ziyu Wang, Xipeng Qiu, Weinan Zhang, Jun Zhu, Zheng Zhang, David Wipf:
Fork or Fail: Cycle-Consistent Training with Many-to-One Mappings. AISTATS 2021: 1828-1836 - [c58]Yu Zhang, Daniel L. Lau, David Wipf:
Sparse Multi-Path Corrections in Fringe Projection Profilometry. CVPR 2021: 13344-13353 - [c57]Wei Chen, David Wipf, Miguel Rodrigues:
Deep Learning for Linear Inverse Problems Using the Plug-and-Play Priors Framework. ICASSP 2021: 8098-8102 - [c56]Yifan Xing, Tong He, Tianjun Xiao, Yongxin Wang, Yuanjun Xiong, Wei Xia, David Wipf, Zheng Zhang, Stefano Soatto:
Learning Hierarchical Graph Neural Networks for Image Clustering. ICCV 2021: 3447-3457 - [c55]Yongyi Yang, Tang Liu, Yangkun Wang, Jinjing Zhou, Quan Gan, Zhewei Wei, Zheng Zhang, Zengfeng Huang, David Wipf:
Graph Neural Networks Inspired by Classical Iterative Algorithms. ICML 2021: 11773-11783 - [c54]Hengrui Zhang, Qitian Wu, Junchi Yan, David Wipf, Philip S. Yu:
From Canonical Correlation Analysis to Self-supervised Graph Neural Networks. NeurIPS 2021: 76-89 - [c53]Bin Dai, Wenliang Li, David P. Wipf:
On the Value of Infinite Gradients in Variational Autoencoder Models. NeurIPS 2021: 7180-7192 - [c52]Qingru Zhang, David Wipf, Quan Gan, Le Song:
A Biased Graph Neural Network Sampler with Near-Optimal Regret. NeurIPS 2021: 8833-8844 - [c51]Longyuan Li, Jian Yao, Li K. Wenliang, Tong He, Tianjun Xiao, Junchi Yan, David Wipf, Zheng Zhang:
GRIN: Generative Relation and Intention Network for Multi-agent Trajectory Prediction. NeurIPS 2021: 27107-27118 - [i30]Qingru Zhang, David Wipf, Quan Gan, Le Song:
A Biased Graph Neural Network Sampler with Near-Optimal Regret. CoRR abs/2103.01089 (2021) - [i29]Yongyi Yang, Tang Liu, Yangkun Wang, Jinjing Zhou, Quan Gan, Zhewei Wei, Zheng Zhang, Zengfeng Huang, David Wipf:
Graph Neural Networks Inspired by Classical Iterative Algorithms. CoRR abs/2103.06064 (2021) - [i28]Hengrui Zhang, Qitian Wu, Junchi Yan, David Wipf, Philip S. Yu:
From Canonical Correlation Analysis to Self-supervised Graph Neural Networks. CoRR abs/2106.12484 (2021) - [i27]Yifan Xing, Tong He, Tianjun Xiao, Yongxin Wang, Yuanjun Xiong, Wei Xia, David Wipf, Zheng Zhang, Stefano Soatto:
Learning Hierarchical Graph Neural Networks for Image Clustering. CoRR abs/2107.01319 (2021) - [i26]Yangkun Wang, Jiarui Jin, Weinan Zhang, Yongyi Yang, Jiuhai Chen, Quan Gan, Yong Yu, Zheng Zhang, Zengfeng Huang, David Wipf:
Why Propagate Alone? Parallel Use of Labels and Features on Graphs. CoRR abs/2110.07190 (2021) - [i25]Jiuhai Chen, Jonas Mueller, Vassilis N. Ioannidis, Soji Adeshina, Yangkun Wang, Tom Goldstein, David Wipf:
Convergent Boosted Smoothing for Modeling Graph Data with Tabular Node Features. CoRR abs/2110.13413 (2021) - [i24]Yongyi Yang, Yangkun Wang, Zengfeng Huang, David Wipf:
Implicit vs Unfolded Graph Neural Networks. CoRR abs/2111.06592 (2021) - [i23]Xiang Song, Runjie Ma, Jiahang Li, Muhan Zhang, David Paul Wipf:
Network In Graph Neural Network. CoRR abs/2111.11638 (2021) - 2020
- [c50]Ziyu Wang, Bin Dai, David Wipf, Jun Zhu:
Further Analysis of Outlier Detection with Deep Generative Models. ICBINB@NeurIPS 2020: 11-20 - [c49]Bin Dai, Ziyu Wang, David P. Wipf:
The Usual Suspects? Reassessing Blame for VAE Posterior Collapse. ICML 2020: 2313-2322 - [c48]Ziyu Wang, Bin Dai, David P. Wipf, Jun Zhu:
Further Analysis of Outlier Detection with Deep Generative Models. NeurIPS 2020 - [i22]Qipeng Guo, Zhijing Jin, Xipeng Qiu, Weinan Zhang, David Wipf, Zheng Zhang:
CycleGT: Unsupervised Graph-to-Text and Text-to-Graph Generation via Cycle Training. CoRR abs/2006.04702 (2020) - [i21]Ziyu Wang, Bin Dai, David P. Wipf, Jun Zhu:
Further Analysis of Outlier Detection with Deep Generative Models. CoRR abs/2010.13064 (2020) - [i20]Qipeng Guo, Zhijing Jin, Ziyu Wang, Xipeng Qiu, Weinan Zhang, Jun Zhu, Zheng Zhang, David Wipf:
Fork or Fail: Cycle-Consistent Training with Many-to-One Mappings. CoRR abs/2012.07412 (2020)
2010 – 2019
- 2019
- [c47]Kaixuan Wei, Jiaolong Yang, Ying Fu, David P. Wipf, Hua Huang:
Single Image Reflection Removal Exploiting Misaligned Training Data and Network Enhancements. CVPR 2019: 8178-8187 - [c46]Wenqi Ren, Jiaolong Yang, Senyou Deng, David P. Wipf, Xiaochun Cao, Xin Tong:
Face Video Deblurring Using 3D Facial Priors. ICCV 2019: 9387-9396 - [c45]Bin Dai, David P. Wipf:
Diagnosing and Enhancing VAE Models. ICLR (Poster) 2019 - [i19]Bin Dai, David P. Wipf:
Diagnosing and Enhancing VAE Models. CoRR abs/1903.05789 (2019) - [i18]Kaixuan Wei, Jiaolong Yang, Ying Fu, David P. Wipf, Hua Huang:
Single Image Reflection Removal Exploiting Misaligned Training Data and Network Enhancements. CoRR abs/1904.00637 (2019) - [i17]Bin Dai, Ziyu Wang, David P. Wipf:
The Usual Suspects? Reassessing Blame for VAE Posterior Collapse. CoRR abs/1912.10702 (2019) - 2018
- [j20]Bin Dai, Yu Wang, John A. D. Aston, Gang Hua, David P. Wipf:
Connections with Robust PCA and the Role of Emergent Sparsity in Variational Autoencoder Models. J. Mach. Learn. Res. 19: 41:1-41:42 (2018) - [j19]Yu Wang, Bin Dai, Gang Hua, John A. D. Aston, David P. Wipf:
Recurrent Variational Autoencoders for Learning Nonlinear Generative Models in the Presence of Outliers. IEEE J. Sel. Top. Signal Process. 12(6): 1615-1627 (2018) - [j18]Qingnan Fan, Jiaolong Yang, David P. Wipf, Baoquan Chen, Xin Tong:
Image smoothing via unsupervised learning. ACM Trans. Graph. 37(6): 259 (2018) - [j17]Bo Xin, Yizhou Wang, Wen Gao, David P. Wipf:
Building Invariances Into Sparse Subspace Clustering. IEEE Trans. Signal Process. 66(2): 449-462 (2018) - [c44]Qingnan Fan, Jiaolong Yang, Gang Hua, Baoquan Chen, David P. Wipf:
Revisiting Deep Intrinsic Image Decompositions. CVPR 2018: 8944-8952 - [c43]Bin Dai, Chen Zhu, Baining Guo, David P. Wipf:
Compressing Neural Networks using the Variational Information Bottleneck. ICML 2018: 1143-1152 - [i16]Bin Dai, Chen Zhu, David P. Wipf:
Compressing Neural Networks using the Variational Information Bottleneck. CoRR abs/1802.10399 (2018) - [i15]Qingnan Fan, Jiaolong Yang, David P. Wipf, Baoquan Chen, Xin Tong:
Image Smoothing via Unsupervised Learning. CoRR abs/1811.02804 (2018) - 2017
- [j16]Dong Chen, Xudong Cao, David P. Wipf, Fang Wen, Jian Sun:
An Efficient Joint Formulation for Bayesian Face Verification. IEEE Trans. Pattern Anal. Mach. Intell. 39(1): 32-46 (2017) - [c42]Qingnan Fan, Jiaolong Yang, Gang Hua, Baoquan Chen, David P. Wipf:
A Generic Deep Architecture for Single Image Reflection Removal and Image Smoothing. ICCV 2017: 3258-3267 - [c41]Hao He, Bo Xin, Satoshi Ikehata, David P. Wipf:
From Bayesian Sparsity to Gated Recurrent Nets. NIPS 2017: 5554-5564 - [c40]Yu Wang, Bin Dai, Gang Hua, John A. D. Aston, David P. Wipf:
Green Generative Modeling: Recycling Dirty Data using Recurrent Variational Autoencoders. UAI 2017 - [c39]Bo Xin, Yizhou Wang, Wen Gao, David P. Wipf:
Data-Dependent Sparsity for Subspace Clustering. UAI 2017 - [i14]Qingnan Fan, David P. Wipf, Gang Hua, Baoquan Chen:
Revisiting Deep Image Smoothing and Intrinsic Image Decomposition. CoRR abs/1701.02965 (2017) - [i13]Hao He, Bo Xin, David P. Wipf:
From Bayesian Sparsity to Gated Recurrent Nets. CoRR abs/1706.02815 (2017) - [i12]Bin Dai, Yu Wang, John A. D. Aston, Gang Hua, David P. Wipf:
Veiled Attributes of the Variational Autoencoder. CoRR abs/1706.05148 (2017) - [i11]Qingnan Fan, Jiaolong Yang, Gang Hua, Baoquan Chen, David P. Wipf:
A Generic Deep Architecture for Single Image Reflection Removal and Image Smoothing. CoRR abs/1708.03474 (2017) - 2016
- [j15]Zhiming Zhou, Guojun Chen, Yue Dong, David P. Wipf, Yong Yu, John M. Snyder, Xin Tong:
Sparse-as-possible SVBRDF acquisition. ACM Trans. Graph. 35(6): 189:1-189:12 (2016) - [j14]Bo Xin, Yizhou Wang, Wen Gao, David P. Wipf:
Exploring Algorithmic Limits of Matrix Rank Minimization Under Affine Constraints. IEEE Trans. Signal Process. 64(19): 4960-4974 (2016) - [j13]Wei Chen, David P. Wipf, Yu Wang, Yang Liu, Ian J. Wassell:
Simultaneous Bayesian Sparse Approximation With Structured Sparse Models. IEEE Trans. Signal Process. 64(23): 6145-6159 (2016) - [c38]David P. Wipf:
Analysis of Variational Bayesian Factorizations for Sparse and Low-Rank Estimation. ICML 2016: 926-935 - [c37]Tianlin Shi, Forest Agostinelli, Matthew Staib, David P. Wipf, Thomas Moscibroda:
Improving Survey Aggregation with Sparsely Represented Signals. KDD 2016: 1845-1854 - [c36]Tae-Hyun Oh, Yasuyuki Matsushita, In-So Kweon, David P. Wipf:
A Pseudo-Bayesian Algorithm for Robust PCA. NIPS 2016: 1390-1398 - [c35]Bo Xin, Yizhou Wang, Wen Gao, David P. Wipf, Baoyuan Wang:
Maximal Sparsity with Deep Networks? NIPS 2016: 4340-4348 - [c34]David P. Wipf, Yue Dong, Bo Xin:
Subspace Clustering with a Twist. UAI 2016 - [i10]Bo Xin, Yizhou Wang, Wen Gao, David P. Wipf:
Maximal Sparsity with Deep Networks? CoRR abs/1605.01636 (2016) - 2015
- [c33]Yi Wu, David P. Wipf, Jeong-Min Yun:
Understanding and Evaluating Sparse Linear Discriminant Analysis. AISTATS 2015 - [c32]David P. Wipf, Jeong-Min Yun, Qing Ling:
Augmented Bayesian Compressive Sensing. DCC 2015: 123-132 - [c31]Huan Yang, Baoyuan Wang, Stephen Lin, David P. Wipf, Minyi Guo, Baining Guo:
Unsupervised Extraction of Video Highlights via Robust Recurrent Auto-Encoders. ICCV 2015: 4633-4641 - [c30]Bo Xin, David P. Wipf:
Pushing the Limits of Affine Rank Minimization by Adapting Probabilistic PCA. ICML 2015: 419-427 - [c29]Yu Wang, David P. Wipf, Qing Ling, Wei Chen, Ian J. Wassell:
Multi-Task Learning for Subspace Segmentation. ICML 2015: 1209-1217 - [c28]Yu Wang, David P. Wipf, Jeong-Min Yun, Wei Chen, Ian J. Wassell:
Clustered Sparse Bayesian Learning. UAI 2015: 932-941 - [i9]Huan Yang, Baoyuan Wang, Stephen Lin, David P. Wipf, Minyi Guo, Baining Guo:
Unsupervised Extraction of Video Highlights Via Robust Recurrent Auto-encoders. CoRR abs/1510.01442 (2015) - [i8]Tae Hyun Oh, David P. Wipf, Yasuyuki Matsushita, In-So Kweon:
New Design Criteria for Robust PCA and a Compliant Bayesian-Inspired Algorithm. CoRR abs/1512.02188 (2015) - 2014
- [j12]David P. Wipf, Haichao Zhang:
Revisiting Bayesian blind deconvolution. J. Mach. Learn. Res. 15(1): 3595-3634 (2014) - [j11]Haichao Zhang, David P. Wipf, Yanning Zhang:
Multi-Observation Blind Deconvolution with an Adaptive Sparse Prior. IEEE Trans. Pattern Anal. Mach. Intell. 36(8): 1628-1643 (2014) - [j10]Satoshi Ikehata, David P. Wipf, Yasuyuki Matsushita, Kiyoharu Aizawa:
Photometric Stereo Using Sparse Bayesian Regression for General Diffuse Surfaces. IEEE Trans. Pattern Anal. Mach. Intell. 36(9): 1816-1831 (2014) - [c27]Yu Wang, David P. Wipf, Wei Chen, Ian J. Wassell:
Exploiting the convex-concave penalty for tracking: A novel dynamic reweighted sparse Bayesian learning algorithm. ICASSP 2014: 3345-3349 - [i7]Bo Xin, David P. Wipf:
Exploring Algorithmic Limits of Matrix Rank Minimization under Affine Constraints. CoRR abs/1406.2504 (2014) - [i6]David P. Wipf:
Non-Convex Rank Minimization via an Empirical Bayesian Approach. CoRR abs/1408.2054 (2014) - 2013
- [c26]Haichao Zhang, David P. Wipf, Yanning Zhang:
Multi-image Blind Deblurring Using a Coupled Adaptive Sparse Prior. CVPR 2013: 1051-1058 - [c25]David P. Wipf, Haichao Zhang:
Analysis of Bayesian Blind Deconvolution. EMMCVPR 2013: 40-53 - [c24]Xudong Cao, David P. Wipf, Fang Wen, Genquan Duan, Jian Sun:
A Practical Transfer Learning Algorithm for Face Verification. ICCV 2013: 3208-3215 - [c23]Quannan Li, Jingdong Wang, David P. Wipf, Zhuowen Tu:
Fixed-Point Model For Structured Labeling. ICML (1) 2013: 214-221 - [c22]Carsten Stahlhut, Hagai Thomas Attias, Kensuke Sekihara, David P. Wipf, Lars Kai Hansen, Srikantan S. Nagarajan:
A hierarchical Bayesian M/EEG imagingmethod correcting for incomplete spatio-temporal priors. ISBI 2013: 560-563 - [c21]Haichao Zhang, David P. Wipf:
Non-Uniform Camera Shake Removal Using a Spatially-Adaptive Sparse Penalty. NIPS 2013: 1556-1564 - [i5]David P. Wipf, Haichao Zhang:
Revisiting Bayesian Blind Deconvolution. CoRR abs/1305.2362 (2013) - [i4]Haichao Zhang, David P. Wipf:
Non-Uniform Blind Deblurring with a Spatially-Adaptive Sparse Prior. CoRR abs/1306.3828 (2013) - 2012
- [j9]Julia P. Owen, David P. Wipf, Hagai Thomas Attias, Kensuke Sekihara, Srikantan S. Nagarajan:
Performance evaluation of the Champagne source reconstruction algorithm on simulated and real M/EEG data. NeuroImage 60(1): 305-323 (2012) - [c20]Satoshi Ikehata, David P. Wipf, Yasuyuki Matsushita, Kiyoharu Aizawa:
Robust photometric stereo using sparse regression. CVPR 2012: 318-325 - [c19]Liwei Wang, Yin Li, Jiaya Jia, Jian Sun, David P. Wipf, James M. Rehg:
Learning sparse covariance patterns for natural scenes. CVPR 2012: 2767-2774 - [c18]David P. Wipf, Yi Wu:
Dual-Space Analysis of the Sparse Linear Model. NIPS 2012: 1754-1762 - [c17]David P. Wipf:
Non-Convex Rank Minimization via an Empirical Bayesian Approach. UAI 2012: 914-923 - [i3]David P. Wipf, Yi Wu:
Dual-Space Analysis of the Sparse Linear Model. CoRR abs/1207.2422 (2012) - [i2]David P. Wipf:
Non-Convex Rank Minimization via an Empirical Bayesian Approach. CoRR abs/1207.2440 (2012) - [i1]Haichao Zhang, David P. Wipf, Yanning Zhang:
Image Super-Resolution via Sparse Bayesian Modeling of Natural Images. CoRR abs/1209.4317 (2012) - 2011
- [j8]David P. Wipf, Bhaskar D. Rao, Srikantan S. Nagarajan:
Latent Variable Bayesian Models for Promoting Sparsity. IEEE Trans. Inf. Theory 57(9): 6236-6255 (2011) - [c16]David P. Wipf:
Sparse Estimation with Structured Dictionaries. NIPS 2011: 2016-2024 - 2010
- [j7]David P. Wipf, Srikantan S. Nagarajan:
Iterative Reweighted 1 and 2 Methods for Finding Sparse Solutions. IEEE J. Sel. Top. Signal Process. 4(2): 317-329 (2010) - [j6]David P. Wipf, Julia P. Owen, Hagai Thomas Attias, Kensuke Sekihara, Srikantan S. Nagarajan:
Robust Bayesian estimation of the location, orientation, and time course of multiple correlated neural sources using MEG. NeuroImage 49(1): 641-655 (2010) - [j5]Matthias W. Seeger, David P. Wipf:
Variational Bayesian Inference Techniques. IEEE Signal Process. Mag. 27(6): 81-91 (2010) - [c15]Carsten Stahlhut, Hagai Attias, David P. Wipf, Lars Kai Hansen, Srikantan S. Nagarajan:
Sparse Spatio-temporal Inference of Electromagnetic Brain Sources. MLMI 2010: 157-164
2000 – 2009
- 2009
- [j4]David P. Wipf, Srikantan S. Nagarajan:
A unified Bayesian framework for MEG/EEG source imaging. NeuroImage 44(3): 947-966 (2009) - [c14]Julia P. Owen, David P. Wipf, Hagai Attias, Kensuke Sekihara, Srikantan S. Nagarajan:
Robust Methods for Reconstructing Brain Activity and Functional Connectivity Between Brain Sources with MEG/EEG Data. ISBI 2009: 1271-1274 - [c13]David P. Wipf, Srikantan S. Nagarajan:
Sparse Estimation Using General Likelihoods and Non-Factorial Priors. NIPS 2009: 2071-2079 - 2008
- [c12]David P. Wipf, Julia P. Owen, Hagai Attias, Kensuke Sekihara, Srikantan S. Nagarajan:
Estimating the Location and Orientation of Complex, Correlated Neural Activity using MEG. NIPS 2008: 1777-1784 - 2007
- [j3]Joel C. McCall, David P. Wipf, Mohan M. Trivedi, Bhaskar D. Rao:
Lane Change Intent Analysis Using Robust Operators and Sparse Bayesian Learning. IEEE Trans. Intell. Transp. Syst. 8(3): 431-440 (2007) - [j2]David P. Wipf, Bhaskar D. Rao:
An Empirical Bayesian Strategy for Solving the Simultaneous Sparse Approximation Problem. IEEE Trans. Signal Process. 55(7-2): 3704-3716 (2007) - [c11]David P. Wipf, Jason A. Palmer, Bhaskar D. Rao, Kenneth Kreutz-Delgado:
Performance Evaluation of Latent Variable Models with Sparse Priors. ICASSP (2) 2007: 453-456 - [c10]David P. Wipf, Srikantan S. Nagarajan:
Beamforming using the relevance vector machine. ICML 2007: 1023-1030 - [c9]David P. Wipf, Srikantan S. Nagarajan:
A New View of Automatic Relevance Determination. NIPS 2007: 1625-1632 - 2006
- [b1]David P. Wipf:
Bayesian methods for finding sparse representations. University of California, San Diego, USA, 2006 - [c8]David P. Wipf, Rey Ramírez, Jason A. Palmer, Scott Makeig, Bhaskar D. Rao:
Analysis of Empirical Bayesian Methods for Neuroelectromagnetic Source Localization. NIPS 2006: 1505-1512 - 2005
- [c7]Joel C. McCall, Mohan M. Trivedi, David P. Wipf, Bhaskar D. Rao:
Lane Change Intent Analysis Using Robust Operators and Sparse Bayesian Learning. CVPR Workshops 2005: 59 - [c6]Jason A. Palmer, David P. Wipf, Kenneth Kreutz-Delgado, Bhaskar D. Rao:
Variational EM Algorithms for Non-Gaussian Latent Variable Models. NIPS 2005: 1059-1066 - [c5]David P. Wipf, Bhaskar D. Rao:
Comparing the Effects of Different Weight Distributions on Finding Sparse Representations. NIPS 2005: 1521-1528 - 2004
- [j1]David P. Wipf, Bhaskar D. Rao:
Sparse Bayesian learning for basis selection. IEEE Trans. Signal Process. 52(8): 2153-2164 (2004) - [c4]David P. Wipf, Bhaskar D. Rao:
Probabilistic analysis for basis selection via ℓp diversity measures. ICASSP (2) 2004: 801-804 - [c3]David P. Wipf, Bhaskar D. Rao:
L_0-norm Minimization for Basis Selection. NIPS 2004: 1513-1520 - 2003
- [c2]David P. Wipf, Bhaskar D. Rao:
Bayesian learning for sparse signal reconstruction. ICASSP (6) 2003: 601-604 - [c1]David P. Wipf, Jason A. Palmer, Bhaskar D. Rao:
Perspectives on Sparse Bayesian Learning. NIPS 2003: 249-256
Coauthor Index
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