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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
[j24]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)- [j23]Jiuhai Chen, Jonas Mueller, Vassilis N. Ioannidis, Tom Goldstein, David Wipf:
Graph Neural Networks Formed via Layer-wise Ensembles of Heterogeneous Base Models. Trans. Mach. Learn. Res. 2024 (2024) - [c82]Amber Yijia Zheng, Tong He, Yixuan Qiu, Minjie Wang, David Wipf:
Graph Machine Learning through the Lens of Bilevel Optimization. AISTATS 2024: 982-990 - [c81]Yanlin Zhang
, Ning Li, Quan Gan, Weinan Zhang, David Wipf, Minjie Wang:
ELF-Gym: Evaluating Large Language Models Generated Features for Tabular Prediction. CIKM 2024: 5420-5424 - [c80]Yixuan He, Gesine Reinert, David Wipf, Mihai Cucuringu:
Robust Angular Synchronization via Directed Graph Neural Networks. ICLR 2024 - [c79]Chenxiao Yang, Qitian Wu, David Wipf, Ruoyu Sun, Junchi Yan:
How Graph Neural Networks Learn: Lessons from Training Dynamics. ICML 2024 - [c78]Quan Gan, Minjie Wang, David Wipf, Christos Faloutsos:
Graph Machine Learning Meets Multi-Table Relational Data. KDD 2024: 6502-6512 - [c77]Han Zhang, Quan Gan, David Wipf, Weinan Zhang:
GFS: Graph-based Feature Synthesis for Prediction over Relational Databases. VLDB Workshops 2024 - [c76]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:
4DBInfer: A 4D Benchmarking Toolbox for Graph-Centric Predictive Modeling on RDBs. VLDB Workshops 2024 - [i55]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) - [i54]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:
4DBInfer: A 4D Benchmarking Toolbox for Graph-Centric Predictive Modeling on Relational DBs. CoRR abs/2404.18209 (2024) - [i53]Xiangkun Hu, Tong He, David Wipf:
New Desiderata for Direct Preference Optimization. CoRR abs/2407.09072 (2024) - [i52]Qitian Wu, Kai Yang, Hengrui Zhang, David Wipf, Junchi Yan:
SGFormer: Single-Layer Graph Transformers with Approximation-Free Linear Complexity. CoRR abs/2409.09007 (2024) - [i51]Qitian Wu, David Wipf, Junchi Yan:
Neural Message Passing Induced by Energy-Constrained Diffusion. CoRR abs/2409.09111 (2024) - [i50]Yanlin Zhang, Ning Li, Quan Gan, Weinan Zhang, David Wipf, Minjie Wang:
ELF-Gym: Evaluating Large Language Models Generated Features for Tabular Prediction. CoRR abs/2410.12865 (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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