David Wipf © All rights reserved.

This page is no longer being updated; please see my google scholar page here.

​For older publications please see my previous academic webpage HERE.

David Wipf

​​​​​​2022

• Yongyi Yang, Yangkun Wang, Zengfeng Huang, David Wipf, "Implicit vs Unfolded Graph Neural Networks," arXiv:2111.06592, 2022.
  
• Yixuan He, Quan Gan, David Wipf, Gesine Reinert, Junchi Yan, Mihai Cucuringu, "GNNRank: Learning Global Rankings from Pairwise Comparisons via Directed Graph Neural Networks," International Conference on Machine Learning, 2022 (to appear).
  
• 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," International Conference on Learning Representations (ICLR), 2022.
• Qitian Wu, Hengrui Zhang, Junchi Yan, David Wipf, "Handling Distribution Shifts on Graphs: An Invariance Perspective," International Conference on Learning Representations (ICLR), 2022.
• Jiarui Jin, Yangkun Wang, Kounianhua Du, Weinan Zhang, Zheng Zhang, David Wipf, Yong Yu, Quan Gan, "Inductive Relation Prediction Using Analogy Subgraph Embeddings," International Conference on Learning Representations (ICLR), 2022.
  
• 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," International Conference on Learning Representations (ICLR), 2022.

​​​​​2021

• Qingru Zhang, David Wipf, Quan Gan, Le Song, "A Biased Graph Neural Network Sampler with Near-Optimal Regret," Advances in Neural Information Processing Systems (NeurIPS), 2021.
  

• Bin Dai, Li K. Wenliang, and David Wipf, "On the Value of Infinite Gradients in Variational Autoencoder Models," Advances in Neural Information Processing Systems (NeurIPS), 2021.
  

• Hengrui Zhang, Qitian Wu, Junchi Yan, David Wipf, Philip S. Yu, "From Canonical Correlation Analysis to Self-Supervised Graph Neural Networks," Advances in Neural Information Processing Systems (NeurIPS), 2021.
  

• ​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," Advances in Neural Information Processing Systems (NeurIPS), 2021.
  

• ​Yangkun Wang, Jiarui Jin, Weinan Zhang, Yong Yu, Zheng Zhang and David Wipf, "Bag of Tricks for Node Classification with Graph Neural Networks," Knowledge Discovery in Data, Deep Learning on Graphs Workshop (DLG-KDD), 2021.
  

• ​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," International Conference on Computer Vision (ICCV), 2021.
  

• 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,"  International Conference on Machine Learning (ICML), 2021.

• 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,"  International Conference on Artificial Intelligence and Statistics (AISTATS), 2021.​​

2020

• Ziyu Wang, Bin Dai, David Wipf, and Jun Zhu, "Further Analysis of Outlier Detection with Deep Generative Models," Advances in Neural Information Processing Systems (NeurIPS), 2020.
• Qipeng Guo, Zhijing Jin, Xipeng Qiu, Weinan Zhang, David Wipf, and Zheng Zhang, "CycleGT:  Unsupervised Graph-to-Text and Text-to-Graph Generation via Cycle Training," INLG Workshop, 2020.
  
• ​Qipeng Guo, Zhijing Jin, Ning Dai, Xipeng Qiu, Xiangyang Xue, David Wipf, and Zheng Zhang, "P2:  A Plan-and-Pretrain Approach for Knowledge Graph-to-Text Generation,"  INLG Workshop, 2020.
• Bin Dai, Ziyu Wang, and David Wipf, "The Usual Suspects?  Reassessing Blame for VAE Posterior Collapse," International Conference on Machine Learning (ICML), 2020.

​​​​​​​​​​​2019

• Bin Dai and David Wipf, "Diagnosing and Enhancing VAE Models," International Conference on Learning Representations (ICLR), 2019.

• Kaixuan Wei, Jiaolong Yang, Ying Fu, David Wipf, and Hua Huang, "Single Image Reflection Removal Exploiting Misaligned Training Data and Network Enhancements," IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), 2019.
  

• Wenqi Ren, Jiaolong Yang, Senyou Deng, David Wipf, Xiaochun Cao, Xin Tong, "Face Video Deblurring Using 3D Facial Priors," IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), 2019.
  

2018

• Yu Wang, Bin Dai, John Aston, Gang Hua, and David Wipf, "Recurrent Variational Autoencoders for Learning Nonlinear Generative Models in the Presence of Outliers," IEEE Journal of Selected Topics in Signal Processing, 2018.
  

• ​​Qingnan Fan, Jiaolong Yang, David Wipf, Baoquan Chen, and Xin Tong, "Image Smoothing via Unsupervised Learning," ACM Transactions on Graphics, 2018.
  

• Bin Dai, Yu Wang, John Aston, Gang Hua, and David Wipf, "Connections with Robust PCA and the Role of Emergent Sparsity in Variational Autoencoder Models," Journal of Machine Learning Research (JMLR), 2018.  For an extended version of this work, please see "Hidden Talents of the Variational Autoencoder."
  

• Bin Dai, Chen Zhu, and David Wipf, ​"Compressing Neural Networks using the Variational Information Bottleneck," International Conference on Machine Learning (ICML), 2018.
  

• Qingnan Fan, Jiaolong Yang, Gang Hua, Baoquan Chen, and David Wipf, "Revisiting Deep Intrinsic Image Decompositions," IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), 2018.
  

• Bo Xin, Yizhou Wang, Wen Gao, and David Wipf, "Building Invariances into Sparse Subspace Clustering," IEEE Transactions on Signal Processing, 2018.
  

2017​​

• Bin Dai, Yu Wang, Gang Hua, John Aston, and David Wipf, "Veiled Attributes of the Variational Autoencoder," arXiv:1706.05148, 2017.​

• ​Hao He, Bo Xin, Satoshi Ikehata, and David Wipf, "From Bayesian Sparsity to Gated Recurrent Nets," Advances in Neural Information Processing Systems (NIPS), 2017.  (older arXiv version) (code)​

• Qingnan Fan, Jiaolong Yang, Gang Hua, Baoquan Chen, and David Wipf, "A Generic Deep Architecture for Single Image Reflection Removal and Image Smoothing," International Conference on Computer Vision (ICCV), 2017.​​

• Yu Wang, Bin Dai, Gang Hua, John Aston, and David Wipf, "Green Generative Modeling: Recycling Dirty Data using Recurrent Variational Autoencoders," Uncertainty in Artificial Intelligence (UAI), 2017.

• ​Bo Xin, Yizhou Wang, Wen Gao, and David Wipf, "Data-Dependent Sparse Subspace Clustering," Uncertainty in Artificial Intelligence (UAI), 2017.
  

• Dong Chen, Xudong Cao, David Wipf, Fang Wen, and Jian Sun, "An Efficient Joint Formulation for Bayesian Face Verification," IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 2017.
  

2016​

• Bo Xin, Yizhou Wang, Wen Gao, Baoyuan Wang, and David Wipf, "Maximal Sparsity with Deep Networks?," Advances in Neural Information Processing Systems (NIPS), 2016.
  
• ​Tae-Hyun Oh, Yasuyuki Matsushita, In So Kweon, and David Wipf, "A Pseudo-Bayesian Algorithm for Robust PCA," Advances in Neural Information Processing Systems (NIPS), 2016.
  
• Bo Xin, Yizhou Wang, Wen Gao, and David Wipf, "Exploring Algorithmic Limits of Matrix Rank Minimization under Affine Constraints," IEEE Transactions on Signal Processing, 2016.

• Tianlin Shi, Forest Agostinelli, Matthew Staib, David Wipf, and Thomas Moscibroda, "Improving Survey Aggregation with Sparsely Represented Signals," Knowledge Discovery and Data Mining (KDD), 2016.
  

• David Wipf, "Analysis of VB Factorizations for Sparse and Low-Rank Estimation," International Conference on Machine Learning (ICML), 2016.
  

• David Wipf, Yue Dong, and Bo Xin, "Subspace Clustering with a Twist," Uncertainty in Artificial Intelligence (UAI), 2016.
  

• Zhiming Zhou, Guojun Chen, Yue Dong, David Wipf, Yong Yu, John Snyder, and Xin Tong, "​Sparse-as-possible SVBRDF acquisition," ACM Transactions on Graphics, 2016. 
  

2015

• Huan Yang, Baoyuan Wang, Stephen Lin, David Wipf, Minyi Guo, and Baining Guo, "Unsupervised Extraction of Video Highlights Via Robust Recurrent Auto-Encoders," International Conference on Computer Vision (ICCV), 2015.
  

• Yu Wang, David Wipf, Jeong-Min Yun, Wei Chen, and Ian Wassell, "Clustered Sparse Bayesian Learning," Uncertainty in Artificial Intelligence (UAI), 2015.
  

• Bo Xin and David Wipf, "Pushing the Limits of Affine Rank Minimization by Adapting Probabilistic PCA," International Conference on Machine Learning (ICML), 2015.
  

• Yu Wang, David Wipf, Qing Ling, Wei Chen, and Ian Wassell, "Multi-Task Learning for Subspace Segmentation," International Conference on Machine Learning (ICML), 2015.
  

• Yi Wu, David Wipf, and Jeong-Min Yun, "Understanding and Evaluating Sparse Linear Discriminant Analysis," Artificial Intelligence and Statistics (AISTATS), 2015.
  

• David Wipf, Jeong-Min Yun, and Qing Ling, "Augmented Bayesian Compressive Sensing," Data Compression Conference (DCC), 2015.
  

2014

• David Wipf and Haichao Zhang, "Revisiting Bayesian Blind Deconvolution," Journal of Machine Learning Research (JMLR), 2014.
  

• Satoshi Ikehata, David Wipf, Yasuyuki Matsushita, and Kiyoharu Aizawa, "Photometric Stereo Using Sparse Bayesian Regression for General Diffuse Surfaces," IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 2014.
  

• Haichao Zhang and David Wipf, "Multi-Observation Blind Deconvolution with an Adaptive Sparse Prior," IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 2014.
  

2013​

• Xudong Cao, David Wipf, Fang Wen, Genquan Duan, and Jian Sun, "A Practical Transfer Learning Algorithm for Face Verification," International Conference on Computer Vision (ICCV), 2013.
  
• Haichao Zhang, David Wipf, and Yanning Zhang, "Multi-Image Blind Deblurring Using a Coupled Adaptive Sparse Prior," IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), 2013.
  
• Quannan Li, Jingdong Wang, David Wipf, and Zhuowen Tu, "Fixed-Point Model for Structured Labeling," International Conference on Machine Learning (ICML), 2013.
  
• Haichao Zhang and David Wipf, "Non-Uniform Camera Shake Removal Using a Spatially-Adaptive Sparse Penalty," Advances in Neural Information Processing Systems (NIPS), 2013.
  

2012

• Liwei Wang, Yin Li, Jiaya Jia, Jian Sun, David Wipf, and James Rehg, "Learning Sparse Covariance Patterns for Natural Scenes," IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), 2012.
  

• David Wipf and Yi Wu, "Dual-Space Analysis of the Sparse Linear Model," Advances in Neural Information Processing Systems (NIPS), 2012.
  

• David Wipf, "Non-Convex Rank Minimization via an Empirical Bayesian Approach," Uncertainty in Artificial Intelligence (UAI), 2012.
  

• Satoshi Ikehata, David Wipf, Yasuyuki Matsushita, and Kiyoharu Aizawa, "Robust Photometric Stereo using Sparse Regression," IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), 2012.
  

• Julia Owen, David Wipf, Hagai Attias, Kensuke Sekihara, and Srikantan Nagarajan, "Performance evaluation of the Champagne source reconstruction algorithm on simulated and real M/EEG data," NeuroImage, 2012.
  

2011

• David Wipf, Bhaskar Rao, and Srikantan Nagarajan, "Latent Variable Bayesian Models for Promoting Sparsity," IEEE Transactions of Information Theory, 2011.
  

• David Wipf, "Sparse Estimation with Structured Dictionaries," Advances in Neural Information Processing Systems (NIPS), 2011.​​