Wipf, David

53 publications

ICLR 2026 On Powerful Ways to Generate: Autoregression, Diffusion, and Beyond Chenxiao Yang, Cai Zhou, David Wipf, Zhiyuan Li

ICLR 2026 TaTToo: Tool-Grounded Thinking PRM for Test-Time Scaling in Tabular Reasoning Jiaru Zou, Soumya Roy, Vinay Kumar Verma, Ziyi Wang, David Wipf, Pan Lu, Sumit Negi, James Zou, Jingrui He

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

AISTATS 2025 Common Learning Constraints Alter Interpretations of Direct Preference Optimization Lemin Kong, Xiangkun Hu, Tong He, David Wipf

ICML 2025 Explicit Preference Optimization: No Need for an Implicit Reward Model Xiangkun Hu, Lemin Kong, Tong He, David Wipf

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

JMLR 2025 Implicit vs Unfolded Graph Neural Networks Yongyi Yang, Tang Liu, Yangkun Wang, Zengfeng Huang, David Wipf

ICLR 2025 MuseGNN: Forming Scalable, Convergent GNN Layers That Minimize a Sampling-Based Energy Haitian Jiang, Renjie Liu, Zengfeng Huang, Yichuan Wang, Xiao Yan, Zhenkun Cai, Minjie Wang, David Wipf

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

ICML 2025 Sparse Autoencoders, Again? Yin Lu, Xuening Zhu, Tong He, David Wipf

JMLR 2025 Transformers from Diffusion: A Unified Framework for Neural Message Passing Qitian Wu, David Wipf, Junchi Yan

NeurIPS 2024 4DBInfer: A 4D Benchmarking Toolbox for Graph-Centric Predictive Modeling on RDBs 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

ICMLW 2024 An In-Context Learning Theoretic Analysis of Chain-of-Thought Chenxiao Yang, Zhiyuan Li, David Wipf

AISTATS 2024 Graph Machine Learning Through the Lens of Bilevel Optimization Amber Yijia Zheng, Tong He, Yixuan Qiu, Minjie Wang, David Wipf

TMLR 2024 Graph Neural Networks Formed via Layer-Wise Ensembles of Heterogeneous Base Models Jiuhai Chen, Jonas Mueller, Vassilis N. Ioannidis, Tom Goldstein, David Wipf

ICML 2024 How Graph Neural Networks Learn: Lessons from Training Dynamics Chenxiao Yang, Qitian Wu, David Wipf, Ruoyu Sun, Junchi Yan

ICMLW 2024 New Desiderata for Direct Preference Optimization Xiangkun Hu, Tong He, David Wipf

ICLR 2024 Robust Angular Synchronization via Directed Graph Neural Networks Yixuan He, Gesine Reinert, David Wipf, Mihai Cucuringu

ICMLW 2024 Unavoidable Learning Constraints Alter the Foundations of Direct Preference Optimization David Wipf

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

ICML 2023 From Hypergraph Energy Functions to Hypergraph Neural Networks Yuxin Wang, Quan Gan, Xipeng Qiu, Xuanjing Huang, David Wipf

ICML 2023 Marginalization Is Not Marginal: No Bad VAE Local Minima When Learning Optimal Sparse Representations David Wipf

ICML 2023 On the Initialization of Graph Neural Networks Jiahang Li, Yakun Song, Xiang Song, David Wipf

ICLR 2022 Does Your Graph Need a Confidence Boost? Convergent Boosted Smoothing on Graphs with Tabular Node Features Jiuhai Chen, Jonas Mueller, Vassilis N. Ioannidis, Soji Adeshina, Yangkun Wang, Tom Goldstein, David Wipf

ICML 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

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

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

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

NeurIPSW 2021 A Closer Look at Distribution Shifts and Out-of-Distribution Generalization on Graphs Mucong Ding, Kezhi Kong, Jiuhai Chen, John Kirchenbauer, Micah Goldblum, David Wipf, Furong Huang, Tom Goldstein

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

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

CVPR 2021 Sparse Multi-Path Corrections in Fringe Projection Profilometry Yu Zhang, Daniel Lau, David Wipf

NeurIPSW 2020 Further Analysis of Outlier Detection with Deep Generative Models Ziyu Wang, Bin Dai, David Wipf, Jun Zhu

ICML 2020 The Usual Suspects? Reassessing Blame for VAE Posterior Collapse Bin Dai, Ziyu Wang, David Wipf

ICLR 2019 Diagnosing and Enhancing VAE Models Bin Dai, David Wipf

ICCV 2019 Face Video Deblurring Using 3D Facial Priors Wenqi Ren, Jiaolong Yang, Senyou Deng, David Wipf, Xiaochun Cao, Xin Tong

CVPR 2019 Single Image Reflection Removal Exploiting Misaligned Training Data and Network Enhancements Kaixuan Wei, Jiaolong Yang, Ying Fu, David Wipf, Hua Huang

ICML 2018 Compressing Neural Networks Using the Variational Information Bottleneck Bin Dai, Chen Zhu, Baining Guo, David Wipf

JMLR 2018 Connections with Robust PCA and the Role of Emergent Sparsity in Variational Autoencoder Models Bin Dai, Yu Wang, John Aston, Gang Hua, David Wipf

CVPR 2018 Revisiting Deep Intrinsic Image Decompositions Qingnan Fan, Jiaolong Yang, Gang Hua, Baoquan Chen, David Wipf

ICCV 2017 A Generic Deep Architecture for Single Image Reflection Removal and Image Smoothing Qingnan Fan, Jiaolong Yang, Gang Hua, Baoquan Chen, David Wipf

NeurIPS 2017 From Bayesian Sparsity to Gated Recurrent Nets Hao He, Bo Xin, Satoshi Ikehata, David Wipf

NeurIPS 2016 A Pseudo-Bayesian Algorithm for Robust PCA Tae-Hyun Oh, Yasuyuki Matsushita, In Kweon, David Wipf

ICML 2016 Analysis of Variational Bayesian Factorizations for Sparse and Low-Rank Estimation David Wipf

NeurIPS 2016 Maximal Sparsity with Deep Networks? Bo Xin, Yizhou Wang, Wen Gao, David Wipf, Baoyuan Wang

ICML 2015 Multi-Task Learning for Subspace Segmentation Yu Wang, David Wipf, Qing Ling, Wei Chen, Ian Wassell

ICML 2015 Pushing the Limits of Affine Rank Minimization by Adapting Probabilistic PCA Bo Xin, David Wipf

ICCV 2015 Unsupervised Extraction of Video Highlights via Robust Recurrent Auto-Encoders Huan Yang, Baoyuan Wang, Stephen Lin, David Wipf, Minyi Guo, Baining Guo

JMLR 2014 Revisiting Bayesian Blind Deconvolution David Wipf, Haichao Zhang

ICCV 2013 A Practical Transfer Learning Algorithm for Face Verification Xudong Cao, David Wipf, Fang Wen, Genquan Duan, Jian Sun

ICML 2013 Fixed-Point Model for Structured Labeling Quannan Li, Jingdong Wang, David Wipf, Zhuowen Tu

CVPR 2013 Multi-Image Blind Deblurring Using a Coupled Adaptive Sparse Prior Haichao Zhang, David Wipf, Yanning Zhang

NeurIPS 2013 Non-Uniform Camera Shake Removal Using a Spatially-Adaptive Sparse Penalty Haichao Zhang, David Wipf