Bayesian learning for sparse signal reconstruction. - David P. Wipf; Bhaskar D. Rao (ICASSP (6), 2003)

Perspectives on Sparse Bayesian Learning. - David P. Wipf; Jason A. Palmer; Bhaskar D. Rao (NIPS, 2003)

Probabilistic analysis for basis selection via ℓp diversity measures. - David P. Wipf; Bhaskar D. Rao (ICASSP (2), 2004)

L_0-norm Minimization for Basis Selection. - David P. Wipf; Bhaskar D. Rao (NIPS, 2004)

Sparse Bayesian learning for basis selection. - David P. Wipf; Bhaskar D. Rao (IEEE Trans. Signal Process., 2004)

Variational EM Algorithms for Non-Gaussian Latent Variable Models. - Jason A. Palmer; David P. Wipf; Kenneth Kreutz-Delgado; Bhaskar D. Rao (NIPS, 2005)

Comparing the Effects of Different Weight Distributions on Finding Sparse Representations. - David P. Wipf; Bhaskar D. Rao (NIPS, 2005)

Analysis of Empirical Bayesian Methods for Neuroelectromagnetic Source Localization. - David P. Wipf; Rey Ramírez; Jason A. Palmer; Scott Makeig; Bhaskar D. Rao (NIPS, 2006)

Bayesian methods for finding sparse representations. - David P. Wipf (2006)

Performance Evaluation of Latent Variable Models with Sparse Priors. - David P. Wipf; Jason A. Palmer; Bhaskar D. Rao; Kenneth Kreutz-Delgado (ICASSP (2), 2007)

Beamforming using the relevance vector machine. - David P. Wipf; Srikantan S. Nagarajan (ICML, 2007)

A New View of Automatic Relevance Determination. - David P. Wipf; Srikantan S. Nagarajan (NIPS, 2007)

Lane Change Intent Analysis Using Robust Operators and Sparse Bayesian Learning. - Joel C. McCall; David P. Wipf; Mohan M. Trivedi; Bhaskar D. Rao (IEEE Trans. Intell. Transp. Syst., 2007)

An Empirical Bayesian Strategy for Solving the Simultaneous Sparse Approximation Problem. - David P. Wipf; Bhaskar D. Rao (IEEE Trans. Signal Process., 2007)

Estimating the Location and Orientation of Complex, Correlated Neural Activity using MEG. - David P. Wipf; Julia P. Owen; Hagai Attias; Kensuke Sekihara; Srikantan S. Nagarajan (NIPS, 2008)

Robust Methods for Reconstructing Brain Activity and Functional Connectivity Between Brain Sources with MEG/EEG Data. - Julia P. Owen; David P. Wipf; Hagai Attias; Kensuke Sekihara; Srikantan S. Nagarajan (ISBI, 2009)

Sparse Estimation Using General Likelihoods and Non-Factorial Priors. - David P. Wipf; Srikantan S. Nagarajan (NIPS, 2009)

A unified Bayesian framework for MEG/EEG source imaging. - David P. Wipf; Srikantan S. Nagarajan (NeuroImage, 2009)

Sparse Spatio-temporal Inference of Electromagnetic Brain Sources. - Carsten Stahlhut; Hagai Attias; David P. Wipf; Lars Kai Hansen; Srikantan S. Nagarajan (MLMI, 2010)

Iterative Reweighted 1 and 2 Methods for Finding Sparse Solutions. - David P. Wipf; Srikantan S. Nagarajan (IEEE J. Sel. Top. Signal Process., 2010)

Robust Bayesian estimation of the location, orientation, and time course of multiple correlated neural sources using MEG. - David P. Wipf; Julia P. Owen; Hagai Thomas Attias; Kensuke Sekihara; Srikantan S. Nagarajan (NeuroImage, 2010)

Variational Bayesian Inference Techniques. - Matthias W. Seeger; David P. Wipf (IEEE Signal Process. Mag., 2010)

Sparse Estimation with Structured Dictionaries. - David P. Wipf (NIPS, 2011)

Latent Variable Bayesian Models for Promoting Sparsity. - David P. Wipf; Bhaskar D. Rao; Srikantan S. Nagarajan (IEEE Trans. Inf. Theory, 2011)

Robust photometric stereo using sparse regression. - Satoshi Ikehata; David P. Wipf; Yasuyuki Matsushita; Kiyoharu Aizawa (CVPR, 2012)

Learning sparse covariance patterns for natural scenes. - Liwei Wang 009; Yin Li 003; Jiaya Jia; Jian Sun 001; David P. Wipf; James M. Rehg (CVPR, 2012)

Dual-Space Analysis of the Sparse Linear Model. - David P. Wipf; Yi Wu 013 (NIPS, 2012)

Non-Convex Rank Minimization via an Empirical Bayesian Approach. - David P. Wipf (UAI, 2012)

Image Super-Resolution via Sparse Bayesian Modeling of Natural Images - Haichao Zhang 001; David P. Wipf; Yanning Zhang 001 (CoRR, 2012)

Performance evaluation of the Champagne source reconstruction algorithm on simulated and real M/EEG data. - Julia P. Owen; David P. Wipf; Hagai Thomas Attias; Kensuke Sekihara; Srikantan S. Nagarajan (NeuroImage, 2012)

Multi-image Blind Deblurring Using a Coupled Adaptive Sparse Prior. - Haichao Zhang 001; David P. Wipf; Yanning Zhang 001 (CVPR, 2013)

Analysis of Bayesian Blind Deconvolution. - David P. Wipf; Haichao Zhang (EMMCVPR, 2013)

A Practical Transfer Learning Algorithm for Face Verification. - Xudong Cao; David P. Wipf; Fang Wen 001; Genquan Duan; Jian Sun 001 (ICCV, 2013)

Fixed-Point Model For Structured Labeling. - Quannan Li; Jingdong Wang 001; David P. Wipf; Zhuowen Tu (ICML (1), 2013)

A hierarchical Bayesian M/EEG imagingmethod correcting for incomplete spatio-temporal priors. - Carsten Stahlhut; Hagai Thomas Attias; Kensuke Sekihara; David P. Wipf; Lars Kai Hansen; Srikantan S. Nagarajan (ISBI, 2013)

Non-Uniform Camera Shake Removal Using a Spatially-Adaptive Sparse Penalty. - Haichao Zhang; David P. Wipf (NIPS, 2013)

Non-Uniform Blind Deblurring with a Spatially-Adaptive Sparse Prior. - Haichao Zhang; David P. Wipf (CoRR, 2013)

Exploiting the convex-concave penalty for tracking: A novel dynamic reweighted sparse Bayesian learning algorithm. - Yu Wang 060; David P. Wipf; Wei Chen 016; Ian J. Wassell (ICASSP, 2014)

Exploring Algorithmic Limits of Matrix Rank Minimization under Affine Constraints. - Bo Xin; David P. Wipf (CoRR, 2014)

Revisiting Bayesian blind deconvolution. - David P. Wipf; Haichao Zhang (J. Mach. Learn. Res., 2014)

Photometric Stereo Using Sparse Bayesian Regression for General Diffuse Surfaces. - Satoshi Ikehata; David P. Wipf; Yasuyuki Matsushita; Kiyoharu Aizawa (IEEE Trans. Pattern Anal. Mach. Intell., 2014)

Multi-Observation Blind Deconvolution with an Adaptive Sparse Prior. - Haichao Zhang 001; David P. Wipf; Yanning Zhang 001 (IEEE Trans. Pattern Anal. Mach. Intell., 2014)

Understanding and Evaluating Sparse Linear Discriminant Analysis. - Yi Wu 013; David P. Wipf; Jeong-Min Yun (AISTATS, 2015)

Augmented Bayesian Compressive Sensing. - David P. Wipf; Jeong-Min Yun; Qing Ling (DCC, 2015)

Unsupervised Extraction of Video Highlights via Robust Recurrent Auto-Encoders. - Huan Yang 005; Baoyuan Wang; Stephen Lin 001; David P. Wipf; Minyi Guo; Baining Guo (ICCV, 2015)

Multi-Task Learning for Subspace Segmentation. - Yu Wang 060; David P. Wipf; Qing Ling; Wei Chen 016; Ian J. Wassell (ICML, 2015)

Pushing the Limits of Affine Rank Minimization by Adapting Probabilistic PCA. - Bo Xin; David P. Wipf (ICML, 2015)

Clustered Sparse Bayesian Learning. - Yu Wang 060; David P. Wipf; Jeong-Min Yun; Wei Chen 016; Ian J. Wassell (UAI, 2015)

New Design Criteria for Robust PCA and a Compliant Bayesian-Inspired Algorithm. - Tae Hyun Oh; David P. Wipf; Yasuyuki Matsushita; In-So Kweon (CoRR, 2015)

Analysis of Variational Bayesian Factorizations for Sparse and Low-Rank Estimation. - David P. Wipf (ICML, 2016)

Improving Survey Aggregation with Sparsely Represented Signals. - Tianlin Shi; Forest Agostinelli; Matthew Staib; David P. Wipf; Thomas Moscibroda (KDD, 2016)

A Pseudo-Bayesian Algorithm for Robust PCA. - Tae-Hyun Oh; Yasuyuki Matsushita; In-So Kweon; David P. Wipf (NIPS, 2016)

Maximal Sparsity with Deep Networks? - Bo Xin; Yizhou Wang 001; Wen Gao 001; David P. Wipf; Baoyuan Wang (NIPS, 2016)

Subspace Clustering with a Twist. - David P. Wipf; Yue Dong 001; Bo Xin (UAI, 2016)

Maximal Sparsity with Deep Networks? - Bo Xin; Yizhou Wang 001; Wen Gao 001; David P. Wipf (CoRR, 2016)

Sparse-as-possible SVBRDF acquisition. - Zhiming Zhou 001; Guojun Chen; Yue Dong 001; David P. Wipf; Yong Yu 001; John M. Snyder; Xin Tong 001 (ACM Trans. Graph., 2016)

Simultaneous Bayesian Sparse Approximation With Structured Sparse Models. - Wei Chen 016; David P. Wipf; Yu Wang 060; Yang Liu 105; Ian J. Wassell (IEEE Trans. Signal Process., 2016)

Exploring Algorithmic Limits of Matrix Rank Minimization Under Affine Constraints. - Bo Xin; Yizhou Wang 001; Wen Gao 001; David P. Wipf (IEEE Trans. Signal Process., 2016)

A Generic Deep Architecture for Single Image Reflection Removal and Image Smoothing. - Qingnan Fan; Jiaolong Yang; Gang Hua 001; Baoquan Chen; David P. Wipf (ICCV, 2017)

From Bayesian Sparsity to Gated Recurrent Nets. - Hao He 011; Bo Xin; Satoshi Ikehata; David P. Wipf (NIPS, 2017)

Green Generative Modeling: Recycling Dirty Data using Recurrent Variational Autoencoders. - Yu Wang 060; Bin Dai; Gang Hua 001; John A. D. Aston; David P. Wipf (UAI, 2017)

Data-Dependent Sparsity for Subspace Clustering. - Bo Xin; Yizhou Wang 001; Wen Gao 001; David P. Wipf (UAI, 2017)

Veiled Attributes of the Variational Autoencoder. - Bin Dai; Yu Wang 060; John A. D. Aston; Gang Hua 001; David P. Wipf (CoRR, 2017)

Revisiting Deep Image Smoothing and Intrinsic Image Decomposition. - Qingnan Fan; David P. Wipf; Gang Hua 001; Baoquan Chen (CoRR, 2017)

From Bayesian Sparsity to Gated Recurrent Nets. - Hao He 011; Bo Xin; David P. Wipf (CoRR, 2017)

An Efficient Joint Formulation for Bayesian Face Verification. - Dong Chen 003; Xudong Cao; David P. Wipf; Fang Wen 001; Jian Sun 001 (IEEE Trans. Pattern Anal. Mach. Intell., 2017)

Revisiting Deep Intrinsic Image Decompositions. - Qingnan Fan; Jiaolong Yang; Gang Hua 001; Baoquan Chen; David P. Wipf (CVPR, 2018)

Compressing Neural Networks using the Variational Information Bottleneck. - Bin Dai; Chen Zhu; Baining Guo; David P. Wipf (ICML, 2018)

Compressing Neural Networks using the Variational Information Bottleneck. - Bin Dai; Chen Zhu; David P. Wipf (CoRR, 2018)

Connections with Robust PCA and the Role of Emergent Sparsity in Variational Autoencoder Models. - Bin Dai; Yu Wang 060; John A. D. Aston; Gang Hua 001; David P. Wipf (J. Mach. Learn. Res., 2018)

Recurrent Variational Autoencoders for Learning Nonlinear Generative Models in the Presence of Outliers. - Yu Wang 060; Bin Dai; Gang Hua 001; John A. D. Aston; David P. Wipf (IEEE J. Sel. Top. Signal Process., 2018)

Image smoothing via unsupervised learning. - Qingnan Fan; Jiaolong Yang; David P. Wipf; Baoquan Chen; Xin Tong 001 (ACM Trans. Graph., 2018)

Building Invariances Into Sparse Subspace Clustering. - Bo Xin; Yizhou Wang 001; Wen Gao 001; David P. Wipf (IEEE Trans. Signal Process., 2018)

Single Image Reflection Removal Exploiting Misaligned Training Data and Network Enhancements. - Kaixuan Wei; Jiaolong Yang; Ying Fu 001; David P. Wipf; Hua Huang 001 (CVPR, 2019)

Face Video Deblurring Using 3D Facial Priors. - Wenqi Ren; Jiaolong Yang; Senyou Deng; David P. Wipf; Xiaochun Cao; Xin Tong 001 (ICCV, 2019)

Diagnosing and Enhancing VAE Models. - Bin Dai; David P. Wipf (ICLR (Poster), 2019)

The Usual Suspects? Reassessing Blame for VAE Posterior Collapse. - Bin Dai; Ziyu Wang 006; David P. Wipf (ICML, 2020)

Further Analysis of Outlier Detection with Deep Generative Models. - Ziyu Wang 006; Bin Dai; David P. Wipf; Jun Zhu 001 (NeurIPS, 2020)

CycleGT: Unsupervised Graph-to-Text and Text-to-Graph Generation via Cycle Training. - Qipeng Guo; Zhijing Jin 001; Xipeng Qiu; Weinan Zhang 001; David Wipf; Zheng Zhang 001 (CoRR, 2020)

Fork or Fail: Cycle-Consistent Training with Many-to-One Mappings. - Qipeng Guo; Zhijing Jin 001; Ziyu Wang 006; Xipeng Qiu; Weinan Zhang 001; Jun Zhu 001; Zheng Zhang 001; David Wipf (AISTATS, 2021)

Sparse Multi-Path Corrections in Fringe Projection Profilometry. - Yu Zhang 009; Daniel L. Lau; David Wipf (CVPR, 2021)

Deep Learning for Linear Inverse Problems Using the Plug-and-Play Priors Framework. - Wei Chen 016; David Wipf; Miguel Rodrigues 001 (ICASSP, 2021)

Learning Hierarchical Graph Neural Networks for Image Clustering. - Yifan Xing; Tong He 002; Tianjun Xiao; Yongxin Wang; Yuanjun Xiong; Wei Xia; David Wipf; Zheng Zhang 001; Stefano Soatto (ICCV, 2021)

Graph Neural Networks Inspired by Classical Iterative Algorithms. - Yongyi Yang; Tang Liu; Yangkun Wang; Jinjing Zhou; Quan Gan; Zhewei Wei; Zheng Zhang 001; Zengfeng Huang; David Wipf (ICML, 2021)

On the Value of Infinite Gradients in Variational Autoencoder Models. - Bin Dai; Wenliang Li; David P. Wipf (NeurIPS, 2021)

GRIN: Generative Relation and Intention Network for Multi-agent Trajectory Prediction. - Longyuan Li; Jian Yao 004; Li K. Wenliang; Tong He 002; Tianjun Xiao; Junchi Yan; David Wipf; Zheng Zhang 001 (NeurIPS, 2021)

A Biased Graph Neural Network Sampler with Near-Optimal Regret. - Qingru Zhang; David Wipf; Quan Gan; Le Song (NeurIPS, 2021)

From Canonical Correlation Analysis to Self-supervised Graph Neural Networks. - Hengrui Zhang; Qitian Wu; Junchi Yan; David Wipf; Philip S. Yu (NeurIPS, 2021)

Convergent Boosted Smoothing for Modeling Graph Data with Tabular Node Features. - Jiuhai Chen; Jonas Mueller 001; Vassilis N. Ioannidis; Soji Adeshina; Yangkun Wang; Tom Goldstein; David Wipf (CoRR, 2021)

Implicit vs Unfolded Graph Neural Networks. - Yongyi Yang; Yangkun Wang; Zengfeng Huang; David Wipf (CoRR, 2021)

Network In Graph Neural Network. - Xiang Song 003; Runjie Ma; Jiahang Li 002; Muhan Zhang; David Paul Wipf (CoRR, 2021)

Does your graph need a confidence boost? Convergent boosted smoothing on graphs with tabular node features. - Jiuhai Chen; Jonas Mueller 001; Vassilis N. Ioannidis; Soji Adeshina; Yangkun Wang; Tom Goldstein; David Wipf (ICLR, 2022)

Inductive Relation Prediction Using Analogy Subgraph Embeddings. - Jiarui Jin; Yangkun Wang; Kounianhua Du; Weinan Zhang 001; Zheng Zhang 001; David Wipf; Yong Yu 001; Quan Gan (ICLR, 2022)

Why Propagate Alone? Parallel Use of Labels and Features on Graphs. - Yangkun Wang; Jiarui Jin; Weinan Zhang 001; Yongyi Yang; Jiuhai Chen; Quan Gan; Yong Yu 001; Zheng Zhang 001; Zengfeng Huang; David Wipf (ICLR, 2022)

Handling Distribution Shifts on Graphs: An Invariance Perspective. - Qitian Wu; Hengrui Zhang; Junchi Yan; David Wipf (ICLR, 2022)

GNNRank: Learning Global Rankings from Pairwise Comparisons via Directed Graph Neural Networks. - Yixuan He; Quan Gan; David Wipf; Gesine D. Reinert; Junchi Yan; Mihai Cucuringu (ICML, 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 001; Ruiwen Zhou; Yangkun Wang; Xilong Zhao; Jiarui Jin; Quan Gan; Zheng Zhang 001; 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)

Transformers from an Optimization Perspective. - Yongyi Yang; Zengfeng Huang; David P. Wipf (NeurIPS, 2022)

Self-supervised Amodal Video Object Segmentation. - Jian Yao 004; Yuxin Hong; Chiyu Wang; Tianjun Xiao; Tong He 002; Francesco Locatello; David P. Wipf; Yanwei Fu 001; Zheng Zhang 001 (NeurIPS, 2022)

Learning Manifold Dimensions with Conditional Variational Autoencoders. - Yijia Zheng 001; Tong He 002; Yixuan Qiu; David P. Wipf (NeurIPS, 2022)

Towards Distribution Shift of Node-Level Prediction on Graphs: An Invariance Perspective. - Qitian Wu; Hengrui Zhang; Junchi Yan; David Wipf (CoRR, 2022)

Structured Graph Variational Autoencoders for Indoor Furniture layout Generation. - Aditya Chattopadhyay; Xi Zhang; David Paul Wipf; Himanshu Arora; René Vidal (CoRR, 2022)

Learning Enhanced Representations for Tabular Data via Neighborhood Propagation. - Kounianhua Du; Weinan Zhang 001; Ruiwen Zhou; Yangkun Wang; Xilong Zhao; Jiarui Jin; Quan Gan; Zheng Zhang 001; David Wipf (CoRR, 2022)

A Robust Stacking Framework for Training Deep Graph Models with Multifaceted Node Features. - Jiuhai Chen; Jonas Mueller 001; Vassilis N. Ioannidis; Tom Goldstein; David Wipf (CoRR, 2022)

Face Restoration via Plug-and-Play 3D Facial Priors. - Xiaobin Hu; Wenqi Ren; Jiaolong Yang; Xiaochun Cao; David Wipf; Bjoern H. Menze; Xin Tong 001; Hongbin Zha (IEEE Trans. Pattern Anal. Mach. Intell., 2022)

DIFFormer: Scalable (Graph) Transformers Induced by Energy Constrained Diffusion. - Qitian Wu; Chenxiao Yang; Wentao Zhao; Yixuan He; David Wipf; Junchi Yan (ICLR, 2023)

On the Initialization of Graph Neural Networks. - Jiahang Li 002; Yakun Song; Xiang Song 003; David Wipf (ICML, 2023)

From Hypergraph Energy Functions to Hypergraph Neural Networks. - Yuxin Wang 005; Quan Gan; Xipeng Qiu; Xuanjing Huang 001; David Wipf (ICML, 2023)

Marginalization is not Marginal: No Bad VAE Local Minima when Learning Optimal Sparse Representations. - David Wipf (ICML, 2023)

Learning Graph Variational Autoencoders with Constraints and Structured Priors for Conditional Indoor 3D Scene Generation. - Aditya Chattopadhyay; Xi Zhang; David Paul Wipf; Himanshu Arora; René Vidal (WACV, 2023)

ReFresh: Reducing Memory Access from Exploiting Stable Historical Embeddings for Graph Neural Network Training. - Kezhao Huang; Haitian Jiang; Minjie Wang; Guangxuan Xiao; David Wipf; Xiang Song 003; Quan Gan; Zengfeng Huang; Jidong Zhai; Zheng Zhang 001 (CoRR, 2023)

How Graph Neural Networks Learn: Lessons from Training Dynamics in Function Space. - Chenxiao Yang; Qitian Wu; David Wipf; Ruoyu Sun 001; Junchi Yan (CoRR, 2023)

MuseGNN: Interpretable and Convergent Graph Neural Network Layers at Scale. - Haitian Jiang; Renjie Liu 001; Xiao Yan 002; Zhenkun Cai; Minjie Wang; David Wipf (CoRR, 2023)

Sparse Bayesian Learning for End-to-End EEG Decoding. - Wenlong Wang; Feifei Qi; David Paul Wipf; Chang Cai; Tianyou Yu; Yuanqing Li 001; Yu Zhang 009; Zhuliang Yu; Wei Wu 022 (IEEE Trans. Pattern Anal. Mach. Intell., 2023)

Graph Machine Learning through the Lens of Bilevel Optimization. - Amber Yijia Zheng; Tong He 002; Yixuan Qiu; Minjie Wang; David Wipf (AISTATS, 2024)

ELF-Gym: Evaluating Large Language Models Generated Features for Tabular Prediction. - Yanlin Zhang; Ning Li 029; Quan Gan; Weinan Zhang 001; David Wipf; Minjie Wang (CIKM, 2024)

Robust Angular Synchronization via Directed Graph Neural Networks. - Yixuan He; Gesine Reinert; David Wipf; Mihai Cucuringu (ICLR, 2024)

How Graph Neural Networks Learn: Lessons from Training Dynamics. - Chenxiao Yang; Qitian Wu; David Wipf; Ruoyu Sun 001; Junchi Yan (ICML, 2024)

Graph Machine Learning Meets Multi-Table Relational Data. - Quan Gan; Minjie Wang; David Wipf; Christos Faloutsos (KDD, 2024)

4DBInfer: A 4D Benchmarking Toolbox for Graph-Centric Predictive Modeling on RDBs. - Minjie Wang; Quan Gan; David Wipf; Zheng Zhang 001; Christos Faloutsos; Weinan Zhang 001; Muhan Zhang; Zhenkun Cai; Jiahang Li 002; Zunyao Mao; Yakun Song; Jianheng Tang; et al. (NeurIPS, 2024)

GFS: Graph-based Feature Synthesis for Prediction over Relational Databases. - Han Zhang 057; Quan Gan; David Wipf; Weinan Zhang 001 (VLDB Workshops, 2024)

BloomGML: Graph Machine Learning through the Lens of Bilevel Optimization. - Amber Yijia Zheng; Tong He 002; Yixuan Qiu; Minjie Wang; David Wipf (CoRR, 2024)

4DBInfer: A 4D Benchmarking Toolbox for Graph-Centric Predictive Modeling on Relational DBs. - Minjie Wang; Quan Gan; David Wipf; Zhenkun Cai; Ning Li 029; Jianheng Tang; Yanlin Zhang; Zizhao Zhang; Zunyao Mao; Yakun Song; Yanbo Wang; Jiahang Li 002; et al. (CoRR, 2024)

New Desiderata for Direct Preference Optimization. - Xiangkun Hu; Tong He 002; David Wipf (CoRR, 2024)

SGFormer: Single-Layer Graph Transformers with Approximation-Free Linear Complexity. - Qitian Wu; Kai Yang; Hengrui Zhang; David Wipf; Junchi Yan (CoRR, 2024)

Neural Message Passing Induced by Energy-Constrained Diffusion. - Qitian Wu; David Wipf; Junchi Yan (CoRR, 2024)

Graph Neural Networks Formed via Layer-wise Ensembles of Heterogeneous Base Models. - Jiuhai Chen; Jonas Mueller 001; Vassilis N. Ioannidis; Tom Goldstein; David Wipf (Trans. Mach. Learn. Res., 2024)

FreshGNN: Reducing Memory Access via Stable Historical Embeddings for Graph Neural Network Training. - Kezhao Huang; Haitian Jiang; Minjie Wang; Guangxuan Xiao; David Wipf; Xiang Song 003; Quan Gan; Zengfeng Huang; Jidong Zhai; Zheng Zhang 001 (Proc. VLDB Endow., 2024)

Common Learning Constraints Alter Interpretations of Direct Preference Optimization. - Lemin Kong; Xiangkun Hu; Tong He 002; David Wipf (AISTATS, 2025)

Prior-Fitted Networks Scale to Larger Datasets When Treated as Weak Learners. - Yuxin Wang 005; Botian Jiang; Yiran Guo; Quan Gan; David Wipf; Xuanjing Huang 001; Xipeng Qiu (AISTATS, 2025)

MuseGNN: Forming Scalable, Convergent GNN Layers that Minimize a Sampling-Based Energy. - Haitian Jiang; Renjie Liu 001; Zengfeng Huang; Yichuan Wang 002; Xiao Yan 002; Zhenkun Cai; Minjie Wang; David Wipf (ICLR, 2025)

Chain-of-Thought Provably Enables Learning the (Otherwise) Unlearnable. - Chenxiao Yang; Zhiyuan Li 005; David Wipf (ICLR, 2025)

Efficient Link Prediction via GNN Layers Induced by Negative Sampling. - Yuxin Wang 005; Xiannian Hu; Quan Gan; Xuanjing Huang 001; Xipeng Qiu; David Wipf (IEEE Trans. Knowl. Data Eng., 2025)

Learning to Fuse Temporal Proximity Networks: A Case Study in Chimpanzee Social Interactions. - Yixuan He; Aaron Sandel; David Wipf; Mihai Cucuringu; John Mitani; Gesine Reinert (CoRR, 2025)

Griffin: Towards a Graph-Centric Relational Database Foundation Model. - Yanbo Wang; Xiyuan Wang; Quan Gan; Minjie Wang; Qibin Yang; David Wipf; Muhan Zhang (CoRR, 2025)

Sparse Autoencoders, Again? - Yin Lu; Xuening Zhu; Tong He 002; David Wipf (CoRR, 2025)

Explicit Preference Optimization: No Need for an Implicit Reward Model. - Xiangkun Hu; Lemin Kong; Tong He 002; David Wipf (CoRR, 2025)

Synthesize, Retrieve, and Propagate: A Unified Predictive Modeling Framework for Relational Databases. - Ning Li 029; Kounianhua Du; Han Zhang 057; Quan Gan; Minjie Wang; David Wipf; Weinan Zhang 001 (CoRR, 2025)