ML Anthology

Wipf, David P.

32 publications

NeurIPS 2022 Descent Steps of a Relation-Aware Energy Produce Heterogeneous Graph Neural Networks Hongjoon Ahn, Yongyi Yang, Quan Gan, Taesup Moon, David P Wipf
NeurIPS 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
NeurIPS 2022 Learning Manifold Dimensions with Conditional Variational Autoencoders Yijia Zheng, Tong He, Yixuan Qiu, David P Wipf
NeurIPS 2022 NodeFormer: A Scalable Graph Structure Learning Transformer for Node Classification Qitian Wu, Wentao Zhao, Zenan Li, David P Wipf, Junchi Yan
NeurIPS 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
NeurIPS 2022 Transformers from an Optimization Perspective Yongyi Yang, Zengfeng Huang, David P Wipf
NeurIPS 2021 A Biased Graph Neural Network Sampler with Near-Optimal Regret Qingru Zhang, David P. Wipf, Quan Gan, Le Song
NeurIPS 2021 From Canonical Correlation Analysis to Self-Supervised Graph Neural Networks Hengrui Zhang, Qitian Wu, Junchi Yan, David P. Wipf, Philip S Yu
NeurIPS 2021 GRIN: Generative Relation and Intention Network for Multi-Agent Trajectory Prediction Longyuan Li, Jian Yao, Li Wenliang, Tong He, Tianjun Xiao, Junchi Yan, David P. Wipf, Zheng Zhang
NeurIPS 2021 On the Value of Infinite Gradients in Variational Autoencoder Models Bin Dai, Li Wenliang, David P. Wipf
NeurIPS 2020 Further Analysis of Outlier Detection with Deep Generative Models Ziyu Wang, Bin Dai, David P. Wipf, Jun Zhu
UAI 2017 Data-Dependent Sparsity for Subspace Clustering Bo Xin, Yizhou Wang, Wen Gao, David P. Wipf
UAI 2017 Green Generative Modeling: Recycling Dirty Data Using Recurrent Variational Autoencoders Yu Wang, Bin Dai, Gang Hua, John A. D. Aston, David P. Wipf
UAI 2016 Subspace Clustering with a Twist David P. Wipf, Yue Dong, Bo Xin
UAI 2015 Clustered Sparse Bayesian Learning Yu Wang, David P. Wipf, Jeong-Min Yun, Wei Chen, Ian J. Wassell
AISTATS 2015 Understanding and Evaluating Sparse Linear Discriminant Analysis Yi Wu, David P. Wipf, Jeong-Min Yun
NeurIPS 2012 Dual-Space Analysis of the Sparse Linear Model Yi Wu, David P. Wipf
CVPR 2012 Learning Sparse Covariance Patterns for Natural Scenes Liwei Wang, Yin Li, Jiaya Jia, Jian Sun, David P. Wipf, James M. Rehg
UAI 2012 Non-Convex Rank Minimization via an Empirical Bayesian Approach David P. Wipf
CVPR 2012 Robust Photometric Stereo Using Sparse Regression Satoshi Ikehata, David P. Wipf, Yasuyuki Matsushita, Kiyoharu Aizawa
NeurIPS 2011 Sparse Estimation with Structured Dictionaries David P. Wipf
NeurIPS 2009 Sparse Estimation Using General Likelihoods and Non-Factorial Priors David P. Wipf, Srikantan S. Nagarajan
NeurIPS 2008 Estimating the Location and Orientation of Complex, Correlated Neural Activity Using MEG Julia Owen, Hagai T. Attias, Kensuke Sekihara, Srikantan S. Nagarajan, David P. Wipf
NeurIPS 2007 A New View of Automatic Relevance Determination David P. Wipf, Srikantan S. Nagarajan
ICML 2007 Beamforming Using the Relevance Vector Machine David P. Wipf, Srikantan S. Nagarajan
NeurIPS 2006 Analysis of Empirical Bayesian Methods for Neuroelectromagnetic Source Localization Rey Ramírez, Jason Palmer, Scott Makeig, Bhaskar D. Rao, David P. Wipf
NeurIPS 2005 Comparing the Effects of Different Weight Distributions on Finding Sparse Representations Bhaskar D. Rao, David P. Wipf
CVPR 2005 Lane Change Intent Analysis Using Robust Operators and Sparse Bayesian Learning Joel C. McCall, Mohan M. Trivedi, David P. Wipf, Bhaskar D. Rao
CVPRW 2005 Lane Change Intent Analysis Using Robust Operators and Sparse Bayesian Learning Joel C. McCall, Mohan M. Trivedi, David P. Wipf, Bhaskar D. Rao
NeurIPS 2005 Variational EM Algorithms for Non-Gaussian Latent Variable Models Jason Palmer, Kenneth Kreutz-Delgado, Bhaskar D. Rao, David P. Wipf
NeurIPS 2004 ℓ₀-Norm Minimization for Basis Selection David P. Wipf, Bhaskar D. Rao
NeurIPS 2003 Perspectives on Sparse Bayesian Learning Jason Palmer, Bhaskar D. Rao, David P. Wipf