2013

• David Wipf and Haichao Zhang, Revisiting Bayesian Blind Deconvolution, in http://arxiv.org/abs/1305.2362, (Conference version will appear in Energy Minimization Methods for Computer Vision and Pattern Recognition), 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, Multi-Image Blind Deblurring Using a Coupled Adaptive Sparse Prior, IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), 2013

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

2011

• David Wipf, Bhaskar Rao, and Srikantan Nagarajan, Latent Variable Bayesian Models for Promoting Sparsity, in IEEE Transactions on Information Theory, September 2011
• David Wipf, Sparse Estimation with Structured Dictionaries, Advances in Neural Information Processing Systems (NIPS), 2011